{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "gpuType": "T4"
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "eb6-HOJWxSbo"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import tensorflow as tf\n",
        "from tensorflow.keras.models import Model\n",
        "from tensorflow.keras.layers import (\n",
        "    Input, Conv1D, MaxPooling1D, LSTM, GRU, Dense, Dropout, BatchNormalization,\n",
        "    Flatten, TimeDistributed, Bidirectional, LayerNormalization, MultiHeadAttention\n",
        ")\n",
        "from sklearn.model_selection import train_test_split\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "from scipy.signal import savgol_filter"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Mount Google Drive for dataset access\n",
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WAXJzVWgDQfQ",
        "outputId": "8b17231c-a8ee-4738-ecd5-607cbc604c2e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Load datasets\n",
        "normal_file_path = '/content/drive/MyDrive/project-3/data/ptbdb_normal.csv'  # Update with the actual path\n",
        "abnormal_file_path = '/content/drive/MyDrive/project-3/data/ptbdb_abnormal.csv'  # Update with the actual path"
      ],
      "metadata": {
        "id": "5DzcLmZWxxUK"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Load normal and abnormal data\n",
        "def load_and_label_data(file_path, label):\n",
        "    data = pd.read_csv(file_path).values\n",
        "    data[:, -1] = label  # Assign the label to the last column\n",
        "    return data\n",
        "\n",
        "normal_data = load_and_label_data(normal_file_path, label=0)\n",
        "abnormal_data = load_and_label_data(abnormal_file_path, label=1)\n",
        "\n",
        "# Combine datasets\n",
        "data = np.vstack((normal_data, abnormal_data))"
      ],
      "metadata": {
        "id": "_Yc02IGuFXIh"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Advanced preprocessing\n",
        "# Apply Savitzky-Golay filtering to smooth signals\n",
        "def preprocess_data(data, label_col=-1):\n",
        "    signals = data[:, :label_col]  # Extract signals\n",
        "    labels = data[:, label_col]   # Extract labels\n",
        "    # Apply smoothing\n",
        "    smoothed_signals = np.array([savgol_filter(signal, window_length=5, polyorder=2) if len(signal) >= 5 else signal for signal in signals])\n",
        "    return smoothed_signals, labels"
      ],
      "metadata": {
        "id": "6Zx-lSWpyBh2"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Preprocess data\n",
        "X_raw, y = preprocess_data(data)"
      ],
      "metadata": {
        "id": "x1z2fMJUCaJ3"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import StandardScaler\n",
        "\n",
        "# Standardize the data\n",
        "scaler = StandardScaler()\n",
        "X_standardized = scaler.fit_transform(X_raw.reshape(-1, X_raw.shape[-1])).reshape(X_raw.shape)"
      ],
      "metadata": {
        "id": "SSBhY6LaWk2m"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Data augmentation\n",
        "# Add random noise to signals\n",
        "noise_factor = 0.01\n",
        "X_augmented = X_standardized + noise_factor * np.random.normal(size=X_standardized.shape)\n",
        "X_combined = np.vstack([X_standardized, X_augmented])\n",
        "y_combined = np.hstack([y, y])"
      ],
      "metadata": {
        "id": "_jT0ry50L0f0"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Reshape data for GRU input (add time dimension)\n",
        "X_combined = np.expand_dims(X_combined, axis=-1)"
      ],
      "metadata": {
        "id": "Rp-ke3ilL9-d"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_train, X_temp, y_train, y_temp = train_test_split(X_combined, y_combined, test_size=0.3, random_state=42)\n",
        "X_val, X_test, y_val, y_test = train_test_split(X_temp, y_temp, test_size=0.5, random_state=42)"
      ],
      "metadata": {
        "id": "g-ZXoLHqCh2i"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def create_model(input_shape):\n",
        "    inputs = Input(shape=input_shape)\n",
        "    x = Bidirectional(GRU(64, return_sequences=True))(inputs)\n",
        "    x = BatchNormalization()(x)\n",
        "    x = Dropout(0.5)(x)\n",
        "    x = GRU(32)(x)\n",
        "    outputs = Dense(1, activation='sigmoid')(x)\n",
        "    model = Model(inputs, outputs)\n",
        "    model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])\n",
        "    return model"
      ],
      "metadata": {
        "id": "P2pHUuGXCjFL"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Create and train the model\n",
        "model = create_model(input_shape=X_train.shape[1:])\n",
        "model.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 354
        },
        "id": "aYnzzVmqCnYc",
        "outputId": "2af41349-2452-49cd-cc1f-f9e04066b353"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1mModel: \"functional\"\u001b[0m\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"functional\"</span>\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓\n",
              "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                        \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape               \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m        Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩\n",
              "│ input_layer_3 (\u001b[38;5;33mInputLayer\u001b[0m)           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m187\u001b[0m, \u001b[38;5;34m1\u001b[0m)              │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ bidirectional_3 (\u001b[38;5;33mBidirectional\u001b[0m)      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m187\u001b[0m, \u001b[38;5;34m128\u001b[0m)            │          \u001b[38;5;34m25,728\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ batch_normalization                  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m187\u001b[0m, \u001b[38;5;34m128\u001b[0m)            │             \u001b[38;5;34m512\u001b[0m │\n",
              "│ (\u001b[38;5;33mBatchNormalization\u001b[0m)                 │                             │                 │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dropout (\u001b[38;5;33mDropout\u001b[0m)                    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m187\u001b[0m, \u001b[38;5;34m128\u001b[0m)            │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ gru_4 (\u001b[38;5;33mGRU\u001b[0m)                          │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m)                  │          \u001b[38;5;34m15,552\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dense (\u001b[38;5;33mDense\u001b[0m)                        │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m)                   │              \u001b[38;5;34m33\u001b[0m │\n",
              "└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓\n",
              "┃<span style=\"font-weight: bold\"> Layer (type)                         </span>┃<span style=\"font-weight: bold\"> Output Shape                </span>┃<span style=\"font-weight: bold\">         Param # </span>┃\n",
              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩\n",
              "│ input_layer_3 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">InputLayer</span>)           │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">187</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>)              │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ bidirectional_3 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Bidirectional</span>)      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">187</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)            │          <span style=\"color: #00af00; text-decoration-color: #00af00\">25,728</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ batch_normalization                  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">187</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)            │             <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span> │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)                 │                             │                 │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dropout (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>)                    │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">187</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)            │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ gru_4 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">GRU</span>)                          │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)                  │          <span style=\"color: #00af00; text-decoration-color: #00af00\">15,552</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dense (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                        │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>)                   │              <span style=\"color: #00af00; text-decoration-color: #00af00\">33</span> │\n",
              "└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m41,825\u001b[0m (163.38 KB)\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">41,825</span> (163.38 KB)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m41,569\u001b[0m (162.38 KB)\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">41,569</span> (162.38 KB)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m256\u001b[0m (1.00 KB)\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">256</span> (1.00 KB)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Callbacks for better training\n",
        "early_stopping = tf.keras.callbacks.EarlyStopping(monitor='val_loss', patience=5, restore_best_weights=True)\n",
        "reduce_lr = tf.keras.callbacks.ReduceLROnPlateau(monitor='val_loss', factor=0.5, patience=3, min_lr=1e-6)"
      ],
      "metadata": {
        "id": "-8YMjb0QMUvM"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Train the model\n",
        "history = model.fit(\n",
        "    X_train, y_train,\n",
        "    validation_data=(X_val, y_val),\n",
        "    epochs=50,\n",
        "    batch_size=32,\n",
        "    callbacks=[early_stopping, reduce_lr]\n",
        ")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PtNKuvs-CyNw",
        "outputId": "a34198eb-fbfa-4c5e-d21a-d8f9a00df630"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/50\n",
            "\u001b[1m637/637\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m20s\u001b[0m 21ms/step - accuracy: 0.7167 - loss: 0.5981 - val_accuracy: 0.7143 - val_loss: 0.5981 - learning_rate: 0.0010\n",
            "Epoch 2/50\n",
            "\u001b[1m637/637\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m14s\u001b[0m 21ms/step - accuracy: 0.7179 - loss: 0.5980 - val_accuracy: 0.7111 - val_loss: 0.5994 - learning_rate: 0.0010\n",
            "Epoch 3/50\n",
            "\u001b[1m637/637\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m13s\u001b[0m 21ms/step - accuracy: 0.7170 - loss: 0.5639 - val_accuracy: 0.7404 - val_loss: 0.4879 - learning_rate: 0.0010\n",
            "Epoch 4/50\n",
            "\u001b[1m637/637\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m22s\u001b[0m 23ms/step - accuracy: 0.7574 - loss: 0.4769 - val_accuracy: 0.8073 - val_loss: 0.4108 - learning_rate: 0.0010\n",
            "Epoch 5/50\n",
            "\u001b[1m637/637\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m20s\u001b[0m 23ms/step - accuracy: 0.8036 - loss: 0.4124 - val_accuracy: 0.8364 - val_loss: 0.3695 - learning_rate: 0.0010\n",
            "Epoch 6/50\n",
            "\u001b[1m637/637\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m20s\u001b[0m 22ms/step - accuracy: 0.8318 - loss: 0.3663 - val_accuracy: 0.8431 - val_loss: 0.3403 - learning_rate: 0.0010\n",
            "Epoch 7/50\n",
            "\u001b[1m637/637\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m21s\u001b[0m 23ms/step - accuracy: 0.8667 - loss: 0.3177 - val_accuracy: 0.8252 - val_loss: 0.3716 - learning_rate: 0.0010\n",
            "Epoch 8/50\n",
            "\u001b[1m637/637\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m22s\u001b[0m 25ms/step - accuracy: 0.8799 - loss: 0.2784 - val_accuracy: 0.9090 - val_loss: 0.2363 - learning_rate: 0.0010\n",
            "Epoch 9/50\n",
            "\u001b[1m637/637\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m20s\u001b[0m 24ms/step - accuracy: 0.8996 - loss: 0.2463 - val_accuracy: 0.9107 - val_loss: 0.2213 - learning_rate: 0.0010\n",
            "Epoch 10/50\n",
            "\u001b[1m637/637\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m20s\u001b[0m 24ms/step - accuracy: 0.9165 - loss: 0.2122 - val_accuracy: 0.9219 - val_loss: 0.2043 - learning_rate: 0.0010\n",
            "Epoch 11/50\n",
            "\u001b[1m637/637\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m15s\u001b[0m 23ms/step - accuracy: 0.9232 - loss: 0.1969 - val_accuracy: 0.9063 - val_loss: 0.2382 - learning_rate: 0.0010\n",
            "Epoch 12/50\n",
            "\u001b[1m637/637\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m21s\u001b[0m 24ms/step - accuracy: 0.9310 - loss: 0.1823 - val_accuracy: 0.9301 - val_loss: 0.1820 - learning_rate: 0.0010\n",
            "Epoch 13/50\n",
            "\u001b[1m637/637\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m20s\u001b[0m 23ms/step - accuracy: 0.9366 - loss: 0.1712 - val_accuracy: 0.9381 - val_loss: 0.1608 - learning_rate: 0.0010\n",
            "Epoch 14/50\n",
            "\u001b[1m637/637\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m21s\u001b[0m 23ms/step - accuracy: 0.9444 - loss: 0.1506 - val_accuracy: 0.9523 - val_loss: 0.1285 - learning_rate: 0.0010\n",
            "Epoch 15/50\n",
            "\u001b[1m637/637\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m20s\u001b[0m 23ms/step - accuracy: 0.9471 - loss: 0.1404 - val_accuracy: 0.9434 - val_loss: 0.1466 - learning_rate: 0.0010\n",
            "Epoch 16/50\n",
            "\u001b[1m637/637\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m21s\u001b[0m 23ms/step - accuracy: 0.9518 - loss: 0.1323 - val_accuracy: 0.9581 - val_loss: 0.1284 - learning_rate: 0.0010\n",
            "Epoch 17/50\n",
            "\u001b[1m637/637\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m21s\u001b[0m 23ms/step - accuracy: 0.9588 - loss: 0.1172 - val_accuracy: 0.9572 - val_loss: 0.1179 - learning_rate: 0.0010\n",
            "Epoch 18/50\n",
            "\u001b[1m637/637\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m20s\u001b[0m 23ms/step - accuracy: 0.9576 - loss: 0.1167 - val_accuracy: 0.9666 - val_loss: 0.1018 - learning_rate: 0.0010\n",
            "Epoch 19/50\n",
            "\u001b[1m637/637\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m21s\u001b[0m 23ms/step - accuracy: 0.9618 - loss: 0.1064 - val_accuracy: 0.9597 - val_loss: 0.1097 - learning_rate: 0.0010\n",
            "Epoch 20/50\n",
            "\u001b[1m637/637\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m21s\u001b[0m 23ms/step - accuracy: 0.9661 - loss: 0.0995 - val_accuracy: 0.9354 - val_loss: 0.1730 - learning_rate: 0.0010\n",
            "Epoch 21/50\n",
            "\u001b[1m637/637\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m14s\u001b[0m 22ms/step - accuracy: 0.9589 - loss: 0.1170 - val_accuracy: 0.9741 - val_loss: 0.0818 - learning_rate: 0.0010\n",
            "Epoch 22/50\n",
            "\u001b[1m637/637\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m21s\u001b[0m 23ms/step - accuracy: 0.9700 - loss: 0.0890 - val_accuracy: 0.9661 - val_loss: 0.0926 - learning_rate: 0.0010\n",
            "Epoch 23/50\n",
            "\u001b[1m637/637\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m20s\u001b[0m 23ms/step - accuracy: 0.9683 - loss: 0.0893 - val_accuracy: 0.9693 - val_loss: 0.0927 - learning_rate: 0.0010\n",
            "Epoch 24/50\n",
            "\u001b[1m637/637\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m22s\u001b[0m 25ms/step - accuracy: 0.9692 - loss: 0.0883 - val_accuracy: 0.9764 - val_loss: 0.0634 - learning_rate: 0.0010\n",
            "Epoch 25/50\n",
            "\u001b[1m637/637\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m20s\u001b[0m 24ms/step - accuracy: 0.9735 - loss: 0.0752 - val_accuracy: 0.9755 - val_loss: 0.0724 - learning_rate: 0.0010\n",
            "Epoch 26/50\n",
            "\u001b[1m637/637\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m20s\u001b[0m 23ms/step - accuracy: 0.9711 - loss: 0.0815 - val_accuracy: 0.9762 - val_loss: 0.0683 - learning_rate: 0.0010\n",
            "Epoch 27/50\n",
            "\u001b[1m637/637\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m20s\u001b[0m 23ms/step - accuracy: 0.9793 - loss: 0.0649 - val_accuracy: 0.9789 - val_loss: 0.0656 - learning_rate: 0.0010\n",
            "Epoch 28/50\n",
            "\u001b[1m637/637\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m20s\u001b[0m 23ms/step - accuracy: 0.9773 - loss: 0.0615 - val_accuracy: 0.9835 - val_loss: 0.0505 - learning_rate: 5.0000e-04\n",
            "Epoch 29/50\n",
            "\u001b[1m637/637\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m15s\u001b[0m 23ms/step - accuracy: 0.9820 - loss: 0.0502 - val_accuracy: 0.9808 - val_loss: 0.0606 - learning_rate: 5.0000e-04\n",
            "Epoch 30/50\n",
            "\u001b[1m637/637\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m20s\u001b[0m 22ms/step - accuracy: 0.9839 - loss: 0.0472 - val_accuracy: 0.9766 - val_loss: 0.0633 - learning_rate: 5.0000e-04\n",
            "Epoch 31/50\n",
            "\u001b[1m637/637\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m14s\u001b[0m 22ms/step - accuracy: 0.9827 - loss: 0.0508 - val_accuracy: 0.9856 - val_loss: 0.0455 - learning_rate: 5.0000e-04\n",
            "Epoch 32/50\n",
            "\u001b[1m637/637\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m21s\u001b[0m 22ms/step - accuracy: 0.9831 - loss: 0.0493 - val_accuracy: 0.9837 - val_loss: 0.0448 - learning_rate: 5.0000e-04\n",
            "Epoch 33/50\n",
            "\u001b[1m637/637\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m21s\u001b[0m 23ms/step - accuracy: 0.9848 - loss: 0.0399 - val_accuracy: 0.9856 - val_loss: 0.0450 - learning_rate: 5.0000e-04\n",
            "Epoch 34/50\n",
            "\u001b[1m637/637\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m14s\u001b[0m 23ms/step - accuracy: 0.9868 - loss: 0.0408 - val_accuracy: 0.9828 - val_loss: 0.0547 - learning_rate: 5.0000e-04\n",
            "Epoch 35/50\n",
            "\u001b[1m637/637\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m21s\u001b[0m 23ms/step - accuracy: 0.9873 - loss: 0.0379 - val_accuracy: 0.9798 - val_loss: 0.0528 - learning_rate: 5.0000e-04\n",
            "Epoch 36/50\n",
            "\u001b[1m637/637\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m21s\u001b[0m 23ms/step - accuracy: 0.9881 - loss: 0.0368 - val_accuracy: 0.9888 - val_loss: 0.0354 - learning_rate: 2.5000e-04\n",
            "Epoch 37/50\n",
            "\u001b[1m637/637\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m14s\u001b[0m 22ms/step - accuracy: 0.9907 - loss: 0.0269 - val_accuracy: 0.9869 - val_loss: 0.0372 - learning_rate: 2.5000e-04\n",
            "Epoch 38/50\n",
            "\u001b[1m637/637\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m14s\u001b[0m 23ms/step - accuracy: 0.9895 - loss: 0.0313 - val_accuracy: 0.9881 - val_loss: 0.0323 - learning_rate: 2.5000e-04\n",
            "Epoch 39/50\n",
            "\u001b[1m637/637\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m14s\u001b[0m 23ms/step - accuracy: 0.9898 - loss: 0.0286 - val_accuracy: 0.9856 - val_loss: 0.0467 - learning_rate: 2.5000e-04\n",
            "Epoch 40/50\n",
            "\u001b[1m637/637\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m14s\u001b[0m 23ms/step - accuracy: 0.9894 - loss: 0.0275 - val_accuracy: 0.9883 - val_loss: 0.0342 - learning_rate: 2.5000e-04\n",
            "Epoch 41/50\n",
            "\u001b[1m637/637\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m21s\u001b[0m 23ms/step - accuracy: 0.9913 - loss: 0.0271 - val_accuracy: 0.9908 - val_loss: 0.0291 - learning_rate: 2.5000e-04\n",
            "Epoch 42/50\n",
            "\u001b[1m637/637\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m20s\u001b[0m 22ms/step - accuracy: 0.9906 - loss: 0.0274 - val_accuracy: 0.9865 - val_loss: 0.0339 - learning_rate: 2.5000e-04\n",
            "Epoch 43/50\n",
            "\u001b[1m637/637\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m21s\u001b[0m 23ms/step - accuracy: 0.9906 - loss: 0.0271 - val_accuracy: 0.9913 - val_loss: 0.0265 - learning_rate: 2.5000e-04\n",
            "Epoch 44/50\n",
            "\u001b[1m637/637\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m20s\u001b[0m 23ms/step - accuracy: 0.9909 - loss: 0.0276 - val_accuracy: 0.9895 - val_loss: 0.0302 - learning_rate: 2.5000e-04\n",
            "Epoch 45/50\n",
            "\u001b[1m637/637\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m20s\u001b[0m 22ms/step - accuracy: 0.9914 - loss: 0.0251 - val_accuracy: 0.9876 - val_loss: 0.0425 - learning_rate: 2.5000e-04\n",
            "Epoch 46/50\n",
            "\u001b[1m637/637\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m21s\u001b[0m 23ms/step - accuracy: 0.9913 - loss: 0.0280 - val_accuracy: 0.9901 - val_loss: 0.0312 - learning_rate: 2.5000e-04\n",
            "Epoch 47/50\n",
            "\u001b[1m637/637\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m21s\u001b[0m 24ms/step - accuracy: 0.9924 - loss: 0.0232 - val_accuracy: 0.9924 - val_loss: 0.0236 - learning_rate: 1.2500e-04\n",
            "Epoch 48/50\n",
            "\u001b[1m637/637\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m21s\u001b[0m 25ms/step - accuracy: 0.9936 - loss: 0.0185 - val_accuracy: 0.9915 - val_loss: 0.0240 - learning_rate: 1.2500e-04\n",
            "Epoch 49/50\n",
            "\u001b[1m637/637\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m20s\u001b[0m 24ms/step - accuracy: 0.9930 - loss: 0.0203 - val_accuracy: 0.9920 - val_loss: 0.0253 - learning_rate: 1.2500e-04\n",
            "Epoch 50/50\n",
            "\u001b[1m637/637\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m21s\u001b[0m 25ms/step - accuracy: 0.9926 - loss: 0.0204 - val_accuracy: 0.9918 - val_loss: 0.0223 - learning_rate: 1.2500e-04\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Evaluate the model\n",
        "loss, accuracy = model.evaluate(X_test, y_test)\n",
        "print(f\"Test Accuracy: {accuracy:.2f}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QckWfs1wCzl_",
        "outputId": "3f3bffc3-af5a-40d3-9e16-1ace034553ea"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1m137/137\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - accuracy: 0.9929 - loss: 0.0237\n",
            "Test Accuracy: 0.99\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Plot training and validation accuracy and loss\n",
        "plt.figure(figsize=(12, 4))\n",
        "\n",
        "# Accuracy plot\n",
        "plt.subplot(1, 2, 1)\n",
        "plt.plot(history.history['accuracy'], label='Train Accuracy')\n",
        "plt.plot(history.history['val_accuracy'], label='Validation Accuracy')\n",
        "plt.legend()\n",
        "plt.title('Accuracy')\n",
        "\n",
        "# Loss plot\n",
        "plt.subplot(1, 2, 2)\n",
        "plt.plot(history.history['loss'], label='Train Loss')\n",
        "plt.plot(history.history['val_loss'], label='Validation Loss')\n",
        "plt.legend()\n",
        "plt.title('Loss')\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 261
        },
        "id": "zh5gMPSaC6eG",
        "outputId": "a5ee3623-8916-4c0c-bdaf-fa5aacbd7fa5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x400 with 2 Axes>"
            ],
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAA94AAAF2CAYAAACYvUCBAAAAOnRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjEwLjAsIGh0dHBzOi8vbWF0cGxvdGxpYi5vcmcvlHJYcgAAAAlwSFlzAAAPYQAAD2EBqD+naQAAtuJJREFUeJzs3Xd8VFX6x/HPzKT3ShJCIBB674gCgqJgQURQRFYUFX8WXJV1VRQFdZXdVVlc666KWECwgLqiKCJVOkivoYWSCqT3zP39cZMJgVCCSSbl+3697uve3HvunWdimTxzznmOxTAMAxERERERERGpElZnByAiIiIiIiJSlynxFhEREREREalCSrxFREREREREqpASbxEREREREZEqpMRbREREREREpAop8RYRERERERGpQkq8RURERERERKqQEm8RERERERGRKqTEW0RERERERKQKKfEWERERERERqUJKvEVqkHfeeQeLxUKvXr2cHYqIiIjUUDNnzsRisbBhwwZnhyIiF0mJt0gNMmvWLKKjo1m3bh2xsbHODkdERERERCqBEm+RGuLgwYOsWrWKadOmERoayqxZs5wdUrmysrKcHYKIiIiISK2ixFukhpg1axaBgYHccMMNjBgxotzEOzU1lccff5zo6Gjc3d1p1KgRY8aMISUlxdEmNzeXKVOm0LJlSzw8PIiIiOCWW25h//79ACxduhSLxcLSpUvLPPvQoUNYLBZmzpzpOHf33Xfj4+PD/v37uf766/H19WX06NEArFixgltvvZXGjRvj7u5OVFQUjz/+ODk5OWfFvXv3bm677TZCQ0Px9PSkVatWPPvsswAsWbIEi8XC/Pnzz7pv9uzZWCwWVq9eXeHfp4iISH32+++/c9111+Hn54ePjw9XX301a9asKdOmoKCAF154gRYtWuDh4UFwcDB9+vRh0aJFjjYJCQmMHTuWRo0a4e7uTkREBEOHDuXQoUPV/I5EajcXZwcgIqZZs2Zxyy234ObmxqhRo3j33XdZv349PXr0ACAzM5O+ffuya9cu7rnnHrp27UpKSgrfffcdR48eJSQkhKKiIm688UYWL17M7bffzqOPPkpGRgaLFi1i+/btxMTEVDiuwsJCBg0aRJ8+fXjttdfw8vIC4MsvvyQ7O5sHH3yQ4OBg1q1bx5tvvsnRo0f58ssvHfdv3bqVvn374urqyv333090dDT79+/nf//7Hy+//DL9+/cnKiqKWbNmMWzYsLN+JzExMfTu3fsP/GZFRETqlx07dtC3b1/8/Px48skncXV15T//+Q/9+/dn2bJljloyU6ZMYerUqdx333307NmT9PR0NmzYwKZNm7jmmmsAGD58ODt27OCRRx4hOjqapKQkFi1aRFxcHNHR0U58lyK1jCEiTrdhwwYDMBYtWmQYhmHY7XajUaNGxqOPPupo8/zzzxuAMW/evLPut9vthmEYxowZMwzAmDZt2jnbLFmyxACMJUuWlLl+8OBBAzA++ugjx7m77rrLAIynn376rOdlZ2efdW7q1KmGxWIxDh8+7DjXr18/w9fXt8y50+MxDMOYOHGi4e7ubqSmpjrOJSUlGS4uLsbkyZPPeh0REZH67KOPPjIAY/369eVev/nmmw03Nzdj//79jnPHjx83fH19jX79+jnOderUybjhhhvO+TqnTp0yAOPVV1+tvOBF6ikNNRepAWbNmkVYWBgDBgwAwGKxMHLkSObMmUNRUREAX3/9NZ06dTqrV7ikfUmbkJAQHnnkkXO2uRQPPvjgWec8PT0dx1lZWaSkpHD55ZdjGAa///47AMnJySxfvpx77rmHxo0bnzOeMWPGkJeXx1dffeU4N3fuXAoLC/nTn/50yXGLiIjUN0VFRfz888/cfPPNNGvWzHE+IiKCO+64g5UrV5Keng5AQEAAO3bsYN++feU+y9PTEzc3N5YuXcqpU6eqJX6RukqJt4iTFRUVMWfOHAYMGMDBgweJjY0lNjaWXr16kZiYyOLFiwHYv38/7du3P++z9u/fT6tWrXBxqbxZJC4uLjRq1Ois83Fxcdx9990EBQXh4+NDaGgoV155JQBpaWkAHDhwAOCCcbdu3ZoePXqUmdc+a9YsLrvsMpo3b15Zb0VERKTOS05OJjs7m1atWp11rU2bNtjtdo4cOQLAiy++SGpqKi1btqRDhw789a9/ZevWrY727u7u/OMf/+DHH38kLCyMfv368c9//pOEhIRqez8idYUSbxEn+/XXX4mPj2fOnDm0aNHCsd12220AlV7d/Fw93yU962dyd3fHarWe1faaa65hwYIFPPXUU3zzzTcsWrTIUZjNbrdXOK4xY8awbNkyjh49yv79+1mzZo16u0VERKpQv3792L9/PzNmzKB9+/Z88MEHdO3alQ8++MDR5rHHHmPv3r1MnToVDw8PnnvuOdq0aeMY3SYiF0fF1UScbNasWTRo0IC33377rGvz5s1j/vz5vPfee8TExLB9+/bzPismJoa1a9dSUFCAq6truW0CAwMBs0L66Q4fPnzRMW/bto29e/fy8ccfM2bMGMf506ugAo4hbheKG+D2229nwoQJfP755+Tk5ODq6srIkSMvOiYRERGB0NBQvLy82LNnz1nXdu/ejdVqJSoqynEuKCiIsWPHMnbsWDIzM+nXrx9Tpkzhvvvuc7SJiYnhL3/5C3/5y1/Yt28fnTt35vXXX+ezzz6rlvckUheox1vEiXJycpg3bx433ngjI0aMOGsbP348GRkZfPfddwwfPpwtW7aUu+yWYRiAWXk0JSWFt95665xtmjRpgs1mY/ny5WWuv/POOxcdt81mK/PMkuM33nijTLvQ0FD69evHjBkziIuLKzeeEiEhIVx33XV89tlnzJo1i8GDBxMSEnLRMYmIiIj5GX3ttdfy7bffllnyKzExkdmzZ9OnTx/8/PwAOHHiRJl7fXx8aN68OXl5eQBkZ2eTm5tbpk1MTAy+vr6ONiJycdTjLeJE3333HRkZGdx0003lXr/ssssIDQ1l1qxZzJ49m6+++opbb72Ve+65h27dunHy5Em+++473nvvPTp16sSYMWP45JNPmDBhAuvWraNv375kZWXxyy+/8NBDDzF06FD8/f259dZbefPNN7FYLMTExPD999+TlJR00XG3bt2amJgYnnjiCY4dO4afnx9ff/11uYVX/v3vf9OnTx+6du3K/fffT9OmTTl06BALFixg8+bNZdqOGTOGESNGAPDSSy9d/C9SRESkHpoxYwYLFy486/yUKVNYtGgRffr04aGHHsLFxYX//Oc/5OXl8c9//tPRrm3btvTv359u3boRFBTEhg0b+Oqrrxg/fjwAe/fu5eqrr+a2226jbdu2uLi4MH/+fBITE7n99tur7X2K1AnOLKkuUt8NGTLE8PDwMLKyss7Z5u677zZcXV2NlJQU48SJE8b48eONyMhIw83NzWjUqJFx1113GSkpKY722dnZxrPPPms0bdrUcHV1NcLDw40RI0aUWVIkOTnZGD58uOHl5WUEBgYa//d//2ds37693OXEvL29y41r586dxsCBAw0fHx8jJCTEGDdunLFly5aznmEYhrF9+3Zj2LBhRkBAgOHh4WG0atXKeO655856Zl5enhEYGGj4+/sbOTk5F/lbFBERqV9KlhM713bkyBFj06ZNxqBBgwwfHx/Dy8vLGDBggLFq1aoyz/nb3/5m9OzZ0wgICDA8PT2N1q1bGy+//LKRn59vGIZhpKSkGA8//LDRunVrw9vb2/D39zd69eplfPHFF8542yK1msUwzhjvKSLiJIWFhTRs2JAhQ4bw4YcfOjscEREREZFKoTneIlJjfPPNNyQnJ5cp2CYiIiIiUtupx1tEnG7t2rVs3bqVl156iZCQEDZt2uTskEREREREKo16vEXE6d59910efPBBGjRowCeffOLscEREREREKpV6vEVERERERESqkHq8RURERERERKqQEm8RERERERGRKuTi7AAqg91u5/jx4/j6+mKxWJwdjoiICIZhkJGRQcOGDbFa9T13ZdDnvYiI1CQV+ayvE4n38ePHiYqKcnYYIiIiZzly5AiNGjVydhh1gj7vRUSkJrqYz/o6kXj7+voC5hv28/NzcjQiIiKQnp5OVFSU4zNK/jh93ouISE1Skc/6OpF4lww38/Pz0wexiIjUKBoSXXn0eS8iIjXRxXzWa9KZiIiIiIiISBWqcOK9fPlyhgwZQsOGDbFYLHzzzTcXvGfp0qV07doVd3d3mjdvzsyZM89q8/bbbxMdHY2Hhwe9evVi3bp1FQ1NREREREREpMapcOKdlZVFp06dePvtty+q/cGDB7nhhhsYMGAAmzdv5rHHHuO+++7jp59+crSZO3cuEyZMYPLkyWzatIlOnToxaNAgkpKSKhqeiIiIiIiISI1iMQzDuOSbLRbmz5/PzTfffM42Tz31FAsWLGD79u2Oc7fffjupqaksXLgQgF69etGjRw/eeustwFwuJCoqikceeYSnn376gnGkp6fj7+9PWlqa5nyJiEiNoM+myqffqYicT1FREQUFBc4OQ+oYV1dXbDZbudcq8rlU5cXVVq9ezcCBA8ucGzRoEI899hgA+fn5bNy4kYkTJzquW61WBg4cyOrVq8t9Zl5eHnl5eY6f09PTKz9wERERERGp8QzDICEhgdTUVGeHInVUQEAA4eHhf6hgapUn3gkJCYSFhZU5FxYWRnp6Ojk5OZw6dYqioqJy2+zevbvcZ06dOpUXXnihymIWEREREZHaoSTpbtCgAV5eXlpNQiqNYRhkZ2c7pkBHRERc8rNq5XJiEydOZMKECY6fS9ZPExERERGR+qOoqMiRdAcHBzs7HKmDPD09AUhKSqJBgwbnHHZ+IVW+nFh4eDiJiYllziUmJuLn54enpychISHYbLZy24SHh5f7THd3d8canlrLU0RExDkquiJJamoqDz/8MBEREbi7u9OyZUt++OGHaopWROqikjndXl5eTo5E6rKSf7/+SA2BKk+8e/fuzeLFi8ucW7RoEb179wbAzc2Nbt26lWljt9tZvHixo42IiIjULBVdkSQ/P59rrrmGQ4cO8dVXX7Fnzx7ef/99IiMjqzlyEamLNLxcqlJl/PtV4aHmmZmZxMbGOn4+ePAgmzdvJigoiMaNGzNx4kSOHTvGJ598AsADDzzAW2+9xZNPPsk999zDr7/+yhdffMGCBQscz5gwYQJ33XUX3bt3p2fPnkyfPp2srCzGjh37h9+giIiIVL5p06Yxbtw4x2f1e++9x4IFC5gxY0a5K5LMmDGDkydPsmrVKlxdXQGIjo6uzpBFREScpsKJ94YNGxgwYIDj55K51nfddRczZ84kPj6euLg4x/WmTZuyYMECHn/8cd544w0aNWrEBx98wKBBgxxtRo4cSXJyMs8//zwJCQl07tyZhQsXnlVwTURE6reEtFy2Hk3FboDNasFmBavFYh4X711sFtxdbLi7WM29q9Vx7OZipaDITnJGHonpuSSdtk9KzyMpI5f0nAK+efgK9Z6cx6WsSPLdd9/Ru3dvHn74Yb799ltCQ0O54447eOqpp845X67KVjGxF0H6ccg+UbydLN6nlJ7LTYNOd0CnkZXzmiIiVSw6OprHHnvMsXqU1CwVTrz79+/P+Zb+njlzZrn3/P777+d97vjx4xk/fnxFwxERkVrAMAzi03LZn5zJoRPZ+Hm40DLMl2ah3ri7nLtISV5hEesPnmL5vmTW7T5MWMoa2lsPctLw5bgRzHEjhHgjmBT8gPMnylbs+JCDK4Xk40oeruTjUu59mXmF+Hq4/sF3XXelpKRUeEWSAwcO8OuvvzJ69Gh++OEHYmNjeeihhygoKGDy5Mnl3lNlq5ikH4fp7S/cLiVWibeIVLoLfbE7efJkpkyZUuHnrl+/Hm9v70uMytS/f386d+7M9OnT/9Bz5Gy1sqq5iIj8MYVFdlxsl1jm4/hmOHUIIrtBQOmKEvmFdk5k5ZGckUfcyWz2J2WxPzmT/cmZHEzJIju/6KxH2awWmgR70bKBLy3DfGgR5kujQE+2HEll2Z4kkg5uo7d9EwOsm3nCuhs3t7OfAZCPK8mWYJKsoZzCF097Dl5GFt5GNl5k40sOvpaccu8tsLhSZHHDbnMDmzsWF3esxpWAEu/KZLfbadCgAf/973+x2Wx069aNY8eO8eqrr54z8a6yVUy8gimyuJDvHgRewbj5hmLzCQav4s3NBxY9B+lHIT8b3FS0SUQqT3x8vON47ty5PP/88+zZs8dxzsfHx3FsGAZFRUW4uFw4bQsNDa3cQKVSKfEWEanDUrPz2ZeUyd7EDOKPHiIo7mfapq+gYdFxfnLpz7LgUTQIDaZJkDdNgr1oHOxFkyAvAr3cyMwvJC27gLScAlKzC8g5FU/M5r/T7Pj3juen2ELZZm3N2sIWrMhtzi6jMfZz1O10KU6ym4Z4cyq7gL2JGWTkFnIgOYsDyVks3AHu5HO5dQcDrJt50bqZKFsynNYhXhQQja1pX8jLgLSjkH4MMhJwo4BII4HIooSyL3oRo8VdjQJcjQKwZ0FJsVI3jwr+puuXS1mRJCIiAldX1zLDytu0aUNCQgL5+fm4ubmddY+7uzvu7u6VGzxwIt9Gt5yPIccCqWCxQGSAJzGhPjRv4ENzbx9udXsNl/w0OHUQwtpVegwiUn+d/v9Jf39/LBaL49zSpUsZMGAAP/zwA5MmTWLbtm38/PPPREVFMWHCBNasWUNWVhZt2rRh6tSpDBw40PGsM4eaWywW3n//fRYsWMBPP/1EZGQkr7/+OjfddNMlx/7111/z/PPPExsbS0REBI888gh/+ctfHNffeecd/vWvf3HkyBH8/f3p27cvX331FQBfffUVL7zwArGxsXh5edGlSxe+/fbbP9xLX1so8RYRqULpuQUkpOVyPDXH3KflkpCWQ3xaLrkFRXi42vBwteHpasPD1Vq8t+HuasNmsWBgYDcAw9yX/GwYUFBkd2x5hXYKigwKCu3kF9nJyivkQEoWnplxDLKuZ7BtPaMssVgtxVOFrDDO/iU3J/3Mv46P4N9F/Sk6LcO1WMzXMJvaucO2mCdd5uJnycZuWNhrNKK55RghRckMKEpmACt42h0yDU92WFty0Ks96aHdsUb1oElEA2JCvYkK8sL1tF52wzBITM9j/9F48nYtJDjuZ1pmrMbTKO2ZtlvdsDTtg6XFtdDiWmzBMWf/kgvzISPeTMTTjkJuqtlj6eEH7r7g7gce/ube3RdsblCUD0V55r2FuebPhXmlxzZ9PJ7P6SuS3HzzzUDpiiTnmjZ2xRVXMHv2bOx2O1ar+e/B3r17iYiIKDfprkqFdoNRPRsTm5RJbFImp7ILOHoqh6Oncli2NxmA1m6hdLGmwYlYJd4itYxhGOQUlD9Cqip5utoqrT7I008/zWuvvUazZs0IDAzkyJEjXH/99bz88su4u7vzySefMGTIEPbs2UPjxo3P+ZwXXniBf/7zn7z66qu8+eabjB49msOHDxMUFFThmDZu3Mhtt93GlClTGDlyJKtWreKhhx4iODiYu+++mw0bNvDnP/+ZTz/9lMsvv5yTJ0+yYsUKwOzlHzVqFP/85z8ZNmwYGRkZrFix4rxTmOsa/WUhIlKJjp7KZtGOeCJXT6ZF5gY225ux0d6SDfZW7DUanbM3+HTe5NDYkkSyEUAK/hWOIcZyjButa5hiW08b97gy104EdCSvxQ34hETi/turhKYf5hXXD3nU5xdmeo3lm6wOxKfnOZLu7q4HecllBm2M/QAccmvJt43+QlZIJyI8imhRuIeozK2EnPodr8SN+ORn0svYQq+sLZA1Cw7bILw9NO4NUb2g8WXg1xCyUrDs+YHwXd8TfmCJmeyW8G0IrQZDi2uxNu0Hbhf4JtzFDQKbmNvFsnqAq3q1/4gLrUgyZswYIiMjmTp1KgAPPvggb731Fo8++iiPPPII+/bt45VXXuHPf/5ztcce5ufB1Fs6On4+mZXvSMJjkzLZl5TBwUPhdCGW3MR9eLSt9hBF5A/IKSii7fM/Vfvr7nxxEF5ulZNevfjii1xzzTWOn4OCgujUqZPj55deeon58+fz3XffnbdO1t13382oUaMAeOWVV/j3v//NunXrGDx4cIVjmjZtGldffTXPPfccAC1btmTnzp28+uqr3H333cTFxeHt7c2NN96Ir68vTZo0oUuXLoCZeBcWFnLLLbfQpIn5ed2hQ4cKx1CbKfEWEfkDDMNgx/F0Fu1MZNHORHbGp/O0y2yudfkeLNDUFs8w228AZFu8OeLTnpNBXciL6InNOwjXtAN4pB/CK+MwPlmH8c+Jw7vgJAB2LBzxas/OgCvZHdCPNI9GWCxgwYLFAq42K24uVtxsFgILEmiVvIhmiQsJyiidJ2ZYbFii+0CbIdD6BoL9GpYG320kbJgBy/5BWM5hnsqbwlNN+pA3+gXSvRoRuPrvuGyaaXZ9u/vD1c8R3f0eHrWeXgytPTDcPLQXQdJOiFtjbkfWQtoRiN9ibmvfM9v5RkBmIhj20scEN4fWN0Kbm6BhF7Be4vxzqTYXWpEkLi7O0bMNEBUVxU8//cTjjz9Ox44diYyM5NFHH+Wpp55y1ltwCPJ2o2fTIHo2Le0B+uiVKMiHlMM7aeTE2ESkfurevXuZnzMzM5kyZQoLFixwJLE5OTllVpMqT8eOpV8yent74+fnR1JS0iXFtGvXLoYOHVrm3BVXXMH06dMpKirimmuuoUmTJjRr1ozBgwczePBghg0bhpeXF506deLqq6+mQ4cODBo0iGuvvZYRI0YQGBh4SbHURkq8RUQuQl5hESmZ+SRnmMXDUjLz2JOQwaKdiRxLLR0afbftJx5wMedAJ/d8Cn83A7fj6+DIerwKsmiVsRYy1sLh987/gp5BWHNO0iR7G02yt3Hd8bcgrAO0udFMohu0haxk2PENbP/KTHJLWF0g5ipoezOWVteB1zmGk7m4wWUPQOdRsPJfsOZdOLwS94+uJtTdD/KKl27qNAqueRF8Gpw/ZqsNwjuYW89x5rm0o6VJeNwaSNxuDgsHiOgErYeY7ye0lTm+XWqV861IsnTp0rPO9e7dmzVr1lRxVJXDu2FrOARFyfucHYqIVJCnq42dLw66cMMqeN3Kcua85yeeeIJFixbx2muv0bx5czw9PRkxYgT5+fnneILJ1bVsoVCLxYLdbj9H6z/G19eXTZs2sXTpUn7++Weef/55pkyZwvr16wkICGDRokWsWrWKn3/+mTfffJNnn32WtWvX0rRp0yqJp6ZR4i0itVNRISyeAhmJENXTHMLcoK2Z/J1PXgYcXQ9xaymM30qmf0uONbqRYy5RpGTmcyIzjxNZ+aRkmsm1mWTnk5ZTcM5Herha6dsilLGBW+m98RPz5NXPE9q3tNgIRYVm0lmSgB5ZCwXZEBQDwTGn7ZuZew9/c8mj3Qtg13dw6DdI3GZuS6eCX6SZwDp6jS0Q3QfaD4e2Q8+dbJf7Bvxh4BTofi8seRm2zDGT7tDWcMPr5nMvlX8j6DDC3MD8/cdvNc9XZGi4SDWLat4eDoFv9vl7k0Sk5rFYLJU25Lum+O2337j77rsZNmwYYPaAHzp0qFpjaNOmDb/99ttZcbVs2dJRONPFxYWBAwcycOBAJk+eTEBAAL/++iu33HILFouFK664giuuuILnn3+eJk2aMH/+/DKrV9RldevfSBGpHwwDfvgLbJxp/rztC3Pv7geNekDjyzCiepEc0J6E+AQKDq3C/fh6gk9uIixnP1bMZNUFCOAHAtZPx2Jvwqai3vyvqDfHKH85DlebhRAfd0J93Qn1cSciwIMrWzagT/MQPBPWwycTAQO63wN9zvgQsblAw87m1uv/Lu59+jU0e457joPsk7DnR9j9PcQuNqt5g7mkV/vh0G6Y2f6PCIiCYe/B5Y9A0i4zgbdV8pJa7r4QfUXlPlOkCrRt3xV+gSAjlfjEJCLCLjDiQ0SkCrVo0YJ58+YxZMgQLBYLzz33XJX1XCcnJ7N58+Yy5yIiIvjLX/5Cjx49eOmllxg5ciSrV6/mrbfe4p133gHg+++/58CBA/Tr14/AwEB++OEH7HY7rVq1Yu3atSxevJhrr72WBg0asHbtWpKTk2nTpk2VvIeaSIm3iFQNwzB7a/f9bCaHbW8Cz4ubx1NkN1h74AQ/bI8nPjWXcH8PGgZ4EhngSWSgJ632vIffxpmABaPHfRQk7sF2fCO2vHTYvxj2L8YChBgWGljOrpZ5xB7KBqMlu+yN6e2yh76WLbS1Hqat9TBPu87hqE9H4iKvJ73p9fiFRJqJtq87/p6u5VcrTd4Ls0eaFbFbXQ/Xv1b5w6a9gqDLaHPLy4Sj6yAw2uwhr2xh7VTFWeo9/4AgTlkCCTROsX3bJiLCKl6ISESkskybNo177rmHyy+/nJCQEJ566inS09Or5LVmz57N7Nmzy5x76aWXmDRpEl988QXPP/88L730EhEREbz44ovcfffdAAQEBDBv3jymTJlCbm4uLVq04PPPP6ddu3bs2rWL5cuXM336dNLT02nSpAmvv/461113XZW8h5rIYtSBGu7p6en4+/uTlpaGn5+fs8MRkcSdsPBpOLis9JzVFZoPNIcct7rurErVRXaDtQdP8MO2eBZuTyAls/w5SyNsy3jN9T8AvOYyjs/s15KaXYCNIlpbjtDduofu1j10s+6loeUkRVg57NKMI76dSA3pSmFkT4LCo2kY4EnDAA98PVzN3uRd38G2r+DQSqD4f4sWK8RcbSa7ra4Hl3LWE85IgA+ugbQ4s7d9zHfg5lUZv0Wp5fTZVPmq+3d69PV+NMrYwscNn+Ou+5+o8tcTkYrLzc3l4MGDNG3aFA8PrVYhVeNc/55V5HNJPd4iUnmyT8KSV2DDh+bcY5s7dLwVjm825zfv/dHcXL0wWl1HTqub2ebRne93nOTH7QmkZOY5HhXg5crgduG0j/QnKT2XY6m5hCSs4K8nPwDg3cIhvJU7ACjAYoGoIF8ahvXCJ+xq7GG+pDbwJcgjAw9vP5q5+3LefmGvIOh2t7mlx8OO+WbBsmMbIXaRuXkEQIdbofMdZtVti8WcrzzrVjPpDoqBUXOVdIvUIW4NWkDGFrIT9mEYRqWtzysiIvWPEm8R+eOKCmHjR2ZhrpxT5rk2N3Gg69MsiHMjpWEerh57aXdyEb2yltCwIB7L9q/x2v41LQ0ffAtvIKtoEP6evgxuF84NHSPoHROMq+20JaWOb4aZU4EijA63MXzgG1yWloerzUrzBj54lFtJ9BJ6xPwioPdD5nZiP2yeZRYbSz8G6983t9A2ZgJ+YAkkbAXvUPjT1+AdfAm/PBGpqYIat4X9EFZwlD2JGbQO18gFERG5NEq8ReTSGQYcWAoLJ0LyLvNUg7bs6DiR1/aFs/TDQ6c1dgduBG6go+UAN9lWcaNtDeGWUzzpOpfHfBdju/IJbD2uPHtI96nDMPs2yM+Epv2wDH2bBi5uNPCv4t7l4Bi4+nkY8Kw5bP73WWZxs+RdsOg5s42rF9wxF4Lqx1IYIvWJS2hzAJpaEli5L0WJt4iIXDIl3iJScSf2w/avzTnRKXsAMDyD2NpyPJPiurHt+ywgGYsFrm4dRutwXwK93Qj2dive9yXIexwBnjbYPR+WvoLbqUPw09Ow5m248knodIdZCTz7JHw2HDITIaw9jPzMXH+6Ollt5rrYMVdBTqo5FH3zLEjZC8M/NIvHiUjdExQDQLQlgX/HpnBf3yooZigiIvWCEm8RuThpx2DHPDPhPv6747Rhc2dnxDAmJF3HnrWuQBYerlZu6x7FPVc0JTrE+9zPBOg0EtrfAr9/Bsv+CWlH4LtHYOV06P80rP8QTuwz160e/aW55rQzeQZA97HmJiJ1W/GqAYGWTHYfPEx+YXfcXKwXuElERORsSrxF6jLDgE2fwNH1ZuLq3wj8I8E/yvz5zEJghgG5qZCVAlnJkJlkzm3evQAOr6K02rcNe9P+LHPrxzO7mxAfa/ZAh/i4cVfvaP50WRMCvSvQK21zNRPZTrfDhhmw4nU4uR/mjTOvu/ubc6j/6DrVIiIV4eaF4ReJJf0Y4QXH2HwklZ5Ng5wdlYiI1EJKvEXqqqJC+P4x+P3Tc7fxDDITcSylyba94NztG/eG9sNZ4XYFz/+SxMGULABiQr0Z17cZN3eJPEeRs4vk6gm9H4auY2DNe7DqTSjKg9tnQYM2l/5cEZFLZAlqBunHaGqJZ2VsihJvERG5JEq8RZytMA/2L4Gmfc9a2/qS5WfDV2Nh70JzLeoe48wENu2oOWQ87YhZqCznpLmdyd0fvEPMat3eIRDVE9rdwv6CQF76fidL9xwEINTXnacGt+aWLpFYrZW4zI67L1z5V7jsQSjMNWMQEXGG4OZwaAXR1gSWxaYw4ZqWzo5IRERqISXeIs627r/w8yRzbeg7vzHnEP8RWSfg85Hm8HIXDxgxA1rfULaNYUBuGqQf4+Tx/WTl2wkIjcQnKByLdyi4epRpnpFbwJu/xvLRb9soKDJwtVm4p09Txg9ojq+H6x+L93zcfcxNRMRZgs0Ca00tCbxxJJWM3IKq/f+eiIjUSUq8RZzt0G/m/vjv8NktcOf8Sy8gduqwWQH8xD7wCDCXuWp82dntLBaSCjyYtrKILzbYsBs2IAkvtxOE+3vQ0N+zeO+Bu6uNj347REpmHgBXtW7ApBva0CxUCbGI1APFlc1buSZRVGCw9sBJBrYNc3JQIiLQv39/OnfuzPTp0wGIjo7mscce47HHHjvnPRaLhfnz53PzzTf/odeurOfUJ0q8RZytpEK4zQ2ObTQT5z/NA48KrhebsA0+GwGZCeDXyCxG1qD1Wc2y8gr5z/IDvL/8ADkFRQAEeLmSml1Adn4RB5KzOJCcddZ9zUK8ee7Gtgxo3aDCb1FEpNYKNtfybkI8YLAyNkWJt4j8IUOGDKGgoICFCxeedW3FihX069ePLVu20LFjxwo9d/369Xh7V9K0xWJTpkzhm2++YfPmzWXOx8fHExgYWKmvdaaZM2fy2GOPkZqaWqWvU12UeIs4U/pxM1G2WOGu72H2beYQ8VkjzMTZ3ffinnNwOcwZDXnp0KAtjP6quGhaqcIiO19sOMq0RXsdvdddGwfw7A1t6NYkiNyCIuLTcolPyyE+tXiflktyRh49mwYxpne0ltERkfonMBosVtztOYSSxsrYFGdHJCK13L333svw4cM5evQojRo1KnPto48+onv37hVOugFCQ0MrK8QLCg8Pr7bXqiv0V7SIM5X0doe2gca9YMy35jDzI2th1m2Ql8nGwyd57pvtfLflOGk5Z1Qcz8uANe+aveR56dDkChj7Y5mk2zAMFu9KZPAbK3hm/jZSMvNoEuzFO6O78vWDl9OtiVmh18PVRtMQby6PCWF4t0aMv6oFLw/rwH/HdOe+vs2UdItI/eTiBgGNAWhmjSc2KZOEtFwnByUitdmNN95IaGgoM2fOLHM+MzOTL7/8knvvvZcTJ04watQoIiMj8fLyokOHDnz++efnfW50dLRj2DnAvn376NevHx4eHrRt25ZFixaddc9TTz1Fy5Yt8fLyolmzZjz33HMUFJh/b86cOZMXXniBLVu2YLFYsFgsjpgtFgvffPON4znbtm3jqquuwtPTk+DgYO6//34yMzMd1++++25uvvlmXnvtNSIiIggODubhhx92vNaliIuLY+jQofj4+ODn58dtt91GYmKi4/qWLVsYMGAAvr6++Pn50a1bNzZs2ADA4cOHGTJkCIGBgXh7e9OuXTt++OGHS47lYqjHW8SZShLvyC7mvmFnc473J8MgbhUJ7w3l7sSHybC78+maw7hYLfRsGsRtkae4Out7fPfOh4LiYeFtboJb3qfQ6sa++HS2HElly9FUNh4+xd5E8398gV6u/PnqFozu1USJtIjIxQqKgVOH6BOUxtoU+C02heHdGl34PhFxDsOAguzqf11XL7BceJUXFxcXxowZw8yZM3n22WexFN/z5ZdfUlRUxKhRo8jMzKRbt2489dRT+Pn5sWDBAu68805iYmLo2bPnBV/Dbrdzyy23EBYWxtq1a0lLSyt37revry8zZ86kYcOGbNu2jXHjxuHr68uTTz7JyJEj2b59OwsXLuSXX34BwN//7DpEWVlZDBo0iN69e7N+/XqSkpK47777GD9+fJkvF5YsWUJERARLliwhNjaWkSNH0rlzZ8aNG3fB91Pe+ytJupctW0ZhYSEPP/wwI0eOZOnSpQCMHj2aLl268O6772Kz2di8eTOurmZxzIcffpj8/HyWL1+Ot7c3O3fuxMenausXKfEWcaZjm8x9wy6l5yK7kX/Hl9g/Hkb4qQ38x/Yq70dPJSUjj9Ynf2H0kV/ofOyAo/kJj2iOthjNAvfr2Pzh72w7luaYu13CzcXK2Cuieah/c/w9VY1XRKRCgmNg/2J6+KWCEm+Rmq8gG15pWP2v+8zxi14a9p577uHVV19l2bJl9O/fHzCHmQ8fPhx/f3/8/f154oknHO0feeQRfvrpJ7744ouLSrx/+eUXdu/ezU8//UTDhubv4pVXXuG6664r027SpEmO4+joaJ544gnmzJnDk08+iaenJz4+Pri4uJx3aPns2bPJzc3lk08+ccwxf+uttxgyZAj/+Mc/CAsz62IEBgby1ltvYbPZaN26NTfccAOLFy++pMR78eLFbNu2jYMHDxIVFQXAJ598Qrt27Vi/fj09evQgLi6Ov/71r7RubdY8atGiheP+uLg4hg8fTocOHQBo1qxZhWOoKCXeIhdSkAvfPmSut33jdPCppPkzhlHa431a4p2YnssDCwqx5PyVT9z+zuW2nfQunIgl/xi4pgFQiAs/FPXks8KrWZfbGtZbgDjHM3zcXegQ6U+nqAA6R/nTPTqIEB/3yolbRKS+KS6w1sKWAMDK2BQMw3D0UomIVFTr1q25/PLLmTFjBv379yc2NpYVK1bw4osvAlBUVMQrr7zCF198wbFjx8jPzycvLw8vL6+Lev6uXbuIiopyJN0AvXv3Pqvd3Llz+fe//83+/fvJzMyksLAQP7+KFfjdtWsXnTp1KlPY7YorrsBut7Nnzx5H4t2uXTtsNpujTUREBNu2bavQa53+mlFRUY6kG6Bt27YEBASwa9cuevTowYQJE7jvvvv49NNPGThwILfeeisxMeZKFX/+85958MEH+fnnnxk4cCDDhw+/pHn1FaHEW+R8DAO+Gw/bvzZ/TtxuFi4LaXH++y5GahzknASrK4S1B2BT3Cke+HQjSRl5+Hm0Zd81H9Nl6T1Yknaa9wRGQ7exuHQeTT+rP/Y9yYTuSiQ+NYe2Df3o1CiAzlEBNAv1wWbVH4QiIpWieEmxwNwjuLtYScrIIzYpkxZhF1kAU0Sql6uX2fvsjNetgHvvvZdHHnmEt99+m48++oiYmBiuvPJKAF599VXeeOMNpk+fTocOHfD29uaxxx4jPz+/0sJdvXo1o0eP5oUXXmDQoEH4+/szZ84cXn/99Up7jdOVDPMuYbFYsNvtVfJaYFZkv+OOO1iwYAE//vgjkydPZs6cOQwbNoz77ruPQYMGsWDBAn7++WemTp3K66+/ziOPPFJl8SjxFjmf5a/Cti/B6gI+4XDqEHx4Ddw+G5pcflGPSM7IY/neZDzdbEQHe9Mk2Atvdxc4XjzMPKwduLjzxfojTPpmO/lFdlqG+fDfO7sTHeIN0f+DLXOg1XXQtD9YzbnZAcDNXSK5uUvkOV5ZREQqRbA5BNF66iC9ogNYHnuSlbEpSrxFaiqL5aKHfDvTbbfdxqOPPsrs2bP55JNPePDBBx0jaX777TeGDh3Kn/70J8Cc07x3717atm17Uc9u06YNR44cIT4+noiICADWrFlTps2qVato0qQJzz77rOPc4cOHy7Rxc3OjqKjsFMbyXmvmzJlkZWU5er1/++03rFYrrVq1uqh4K6rk/R05csTR671z505SU1PL/I5atmxJy5Ytefzxxxk1ahQfffQRw4YNAyAqKooHHniABx54gIkTJ/L+++8r8RZxiu1fw5KXzeMbXodWN8DnI821tj8ZCje/Cx1GlHtrWk4BP21P4Lstx1m1PwW7UfZ6A193nnVbwFBgOzHMmreVz9cdAWBQuzBev60zPu7F/3lGdjM3ERFxDv/G5uikwlyujSpieaw5z3vsFU2dHZmI1GI+Pj6MHDmSiRMnkp6ezt133+241qJFC7766itWrVpFYGAg06ZNIzEx8aIT74EDB9KyZUvuuusuXn31VdLT08sk2CWvERcXx5w5c+jRowcLFixg/vz5ZdpER0dz8OBBNm/eTKNGjfD19cXdvez0xdGjRzN58mTuuusupkyZQnJyMo888gh33nmnY5j5pSoqKjprDXF3d3cGDhxIhw4dGD16NNOnT6ewsJCHHnqIK6+8ku7du5OTk8Nf//pXRowYQdOmTTl69Cjr169n+PDhADz22GNcd911tGzZklOnTrFkyRLatGnzh2K9EJU1FinP0Q3wzUPmce/x0O1uc273Xd9D6xuhKB++vhdWTDOHowM5+UX8b8txxn2ygR5/+4Unv97Kylgz6e4Q6U+XxgEEeplDbJIy8ghJN4ePfxIX5Ei6J1zTkndHdytNukVExPlsLuZUH+CKQLPWxpoDJykoqrohkiJSP9x7772cOnWKQYMGlZmPPWnSJLp27cqgQYPo378/4eHh3HzzzRf9XKvVyvz588nJyaFnz57cd999vPzyy2Xa3HTTTTz++OOMHz+ezp07s2rVKp577rkybYYPH87gwYMZMGAAoaGh5S5p5uXlxU8//cTJkyfp0aMHI0aM4Oqrr+att96q2C+jHJmZmXTp0qXMNmTIECwWC99++y2BgYH069ePgQMH0qxZM+bOnQuAzWbjxIkTjBkzhpYtW3Lbbbdx3XXX8cILLwBmQv/www/Tpk0bBg8eTMuWLXnnnXf+cLznYzEMw7hws5otPT0df39/0tLSKlwMQOQsqUfg/asgKwlaDjaHlVtLC0FgL4Kfn4M1bwOQ0Hwkr7rcz487U8jOLx2K0zLMh5s6NWRIp4Y0CS4d7pSWXcChlAzaftIB18JMXmv6ITuNJozu1Zir2/yxbwVFpObQZ1Plc+rvdPZI2LsQ+/XT6LYwilPZBXz9YG+6NQmq3jhEpIzc3FwOHjxI06ZN8fDwcHY4Uked69+zinwuqVtN5HR5GfD57WbSHdYehn9QNukGsNo4dtlz7EvypO+BaYTHzmVI0W4WFvyZqKAghnRsyE2dG9I6vPz/+Py9XOnkdRIKM8HFgyf+NBRsWuJLRKRGK65sbj25n8ubd2HB1nhW7EtR4i0iIhdFibdICXsRfD3OrFzu3QBGzQH30sI5eYVF/LwjkS82HCleSqYrA62P82/Xt+hv28LqqI/wHff9xS0vU7KMWHhHJd0iIrVBUPEaryf3c0VMCAu2xrPmwAnnxiQiIrWGEm+pH04ehF3/A+8Q8G9kbn6R4HJacYhFz8PeHzFs7hwZ9AH7E905vucwx1NzOHIyh+X7kknNLnA0790smBt63IM18Dr4eBB+x1eaVc+DLqLYTklF89PW7xYRkRos2FxSjBP76X51IABbjqRRWGTHxaaSOSIicn5KvKXusxfBnNGQtOPsa94NsPs14nCOG01TzSUWHsm5n+9nZwLrz2oe4e/BiG6NuLVbFI2DS9ZqbATRV8DB5WZyf8WfLxxTSY+3Em8RkdqheKg5pw7SPNgDXw8XMnIL2Z2QQftIf+fGJiIiNZ4Sb6n7ts41k253P4jsCmlHza0wF7KSsGYlUdJHPa1gBN/be+Pn4ULDAE8aBXrSMMDc2jX04/KYEGzWcoaStx1qJt47v71w4m0vgvgt5nFk10p9qyIiUkV8G4KLBxTmYk0/QpfGgSzfm8ymuFNKvEVE5IKUeEvdVpALvxYvndDvCbjiUQBSs/L413dr2LB1G5GWFFp5pnNlh6bceNldjAvwxNejgvOuWw+BBU/AsQ1mUu/f6Nxtk/dAQTa4+ZT2oIiISM1mtZrzvJN2wokDdGvchOV7k9l4+BRjekc7OzqRes9u1/J+UnUq498vJd5St61/H9KPmvO5e96PYRh8vzWeF/63g5TMfCyWpnTt2Z/7B7eqeLJ9Ot8waHwZxK02h5tf9uC525YMM4/odHbFdBERqbmCY4oT71i6NekMwMbDp5wbk0g95+bmhtVq5fjx44SGhuLm5nZxhW5FLoJhGOTn55OcnIzVasXNze2Sn3VJiffbb7/Nq6++SkJCAp06deLNN9+kZ8+e5bYtKChg6tSpfPzxxxw7doxWrVrxj3/8g8GDBzvaTJkyxbGYeYlWrVqxe/fuSwlPxJRzCpa/Zh4PeIZjWfDcNxv4dXcSAM0b+PCP4R0qbymYtkPNxHvndxeXeGt+t4hI7RJUXGDt5H46dfbHYoGjp3JISs+lgZ/WDxZxBqvVStOmTYmPj+f48ePODkfqKC8vLxo3bozVeunFNCuceM+dO5cJEybw3nvv0atXL6ZPn86gQYPYs2cPDRo0OKv9pEmT+Oyzz3j//fdp3bo1P/30E8OGDWPVqlV06VKaeLRr145ffvmlNDAXdcbLaU4ehP89Cl3HQIcRF3fPyumQm4o9tDUfpPXkjWnLyMovwtVm4eEBzXmwfwzuLpXY49xmCCx82ky+MxLNXvDyqKK5iEjtdFplc18PV1qF+bI7IYNNcacY3D7CubGJ1GNubm40btyYwsJCioqKnB2O1DE2mw0XF5c/PJKiwtnttGnTGDduHGPHjgXgvffeY8GCBcyYMYOnn376rPaffvopzz77LNdffz0ADz74IL/88guvv/46n332WWkgLi6Eh4df6vuQuu7HJ+HgMji0wpwb3Wrw+dunHcNY+x4W4IlTw5i3cB8A3ZoE8vdbOtAizPf8918K/0YQ2d2c5737f9DjvrPbFOZDwnbzWIm3iEjtUlKX40QsAF2bBBYn3qlKvEWczGKx4OrqiqvrH5g6KFKFKtRXnp+fz8aNGxk4cGDpA6xWBg4cyOrVq8u9Jy8vDw+PssOvPD09WblyZZlz+/bto2HDhjRr1ozRo0cTFxdXkdCkLtu/BPb9bB4bdvhqLBzdeM7mdrvB4XnPYSnMZa29NfMy2xMZ4Mlrt3biy//rXTVJd4m2N5n7nd+Vfz15FxTlgYe/WaRHRERqj5Kh5mlHoDCfbo3N9bw1z1tERC6kQol3SkoKRUVFhIWVHUIbFhZGQkJCufcMGjSIadOmsW/fPux2O4sWLWLevHnEx8c72vTq1YuZM2eycOFC3n33XQ4ePEjfvn3JyMgo95l5eXmkp6eX2aSOshfBz8+Zx93vheYDzYrgs2+DE/vLNDUMg8W7EnngX7NodGgeAO+53smUIe349YkrGdGtEdbylgKrTG2KE+9DKyHrxNnXj502zFyFP0REahefBuaoK8MOpw7RrYmZeG87mkZeoYa3iojIuV367PCL9MYbb9CiRQtat26Nm5sb48ePZ+zYsWUmpl933XXceuutdOzYkUGDBvHDDz+QmprKF198Ue4zp06dir+/v2OLioqq6rchzrLlc0jcBu7+MOBZuPVjsxp4dgrMGgFZKRQU2Vm4PYFb31vNvR9v4NbUGdgsBvuDB/DWkw9w9xVNK3cu9/kENYXwjmAUwZ4FZ19XYTURqUPefvttoqOj8fDwoFevXqxbt+6cbWfOnInFYimznTkirsazWE6b5x1Lk2AvgrzdyC+ys+O4OgFEROTcKpR4h4SEYLPZSExMLHM+MTHxnPOzQ0ND+eabb8jKyuLw4cPs3r0bHx8fmjU79zDbgIAAWrZsSWxsbLnXJ06cSFpammM7cuRIRd6G1Bb5WfDr38zjfk+AdzC4+8AdX0JAYzh5gOPvDGHAKz/wwGcb2XD4FJe77OUa20YMi42YUa/i7e6EIn1th5r7nd+efU2Jt4jUESXFVidPnsymTZvo1KkTgwYNIikp6Zz3+Pn5ER8f79gOHz5cjRFXktMqm1ssFroWDzffpOHmIiJyHhVKvN3c3OjWrRuLFy92nLPb7SxevJjevXuf914PDw8iIyMpLCzk66+/ZujQoedsm5mZyf79+4mIKL9Qibu7O35+fmU2qYNWvQUZ8WaS3fN+AHILipgfW8Bjrs9xyvChYdZOpuS/Rpi3jf/r25SZUWYvs6XrnRDSwjlxlyTeB5ZBTmrp+YJcc/1XgIZdqz0sEZHKdHqx1bZt2/Lee+/h5eXFjBkzznmPxWIhPDzcsZ05da1WOK2yOUDXJgEAbIpT4i0iIudW4aHmEyZM4P333+fjjz9m165dPPjgg2RlZTmqnI8ZM4aJEyc62q9du5Z58+Zx4MABVqxYweDBg7Hb7Tz55JOONk888QTLli3j0KFDrFq1imHDhmGz2Rg1alQlvEWplTIS4Lc3zOOBU0jIhsnfbqfHy7/w+NwtfHPEm3EFT5BvcWOg7XdWdfqBic0O4Ba/Hlw84cqzK+xXm5AWENoG7AWwd2Hp+cTtYC8ErxCzArqISC11KcVWwfxivUmTJkRFRTF06FB27Nhx3tepkTVdzqhsfnqBNcMwnBWViIjUcBUehzty5EiSk5N5/vnnSUhIoHPnzixcuNDxrXVcXFyZ+du5ublMmjSJAwcO4OPjw/XXX8+nn35KQECAo83Ro0cZNWoUJ06cIDQ0lD59+rBmzRpCQ0P/+DuU2mnJy1CQhb1hN95J7MDbc5eSU2AWrokM8GRkjyhGdLsKt/jmMPdObJs+hu1fm/f2fgj8nLysS9uhsGyXOdy80+3mudOHmauwmojUYucrtrp79+5y72nVqhUzZsygY8eOpKWl8dprr3H55ZezY8cOGjUq/8vIqVOn8sILL1R6/H+IY6j5AQA6NgrAxWohMT2P42m5RAZ4OjE4ERGpqS5pAuz48eMZP358udeWLl1a5ucrr7ySnTt3nvd5c+bMuZQwpK5K3IHx+2dYgIdSRrDwQOka3I9e3YI+zUNKq5MHDIHrX4UfnoD8TPAMgisedV7sJdreBMv+DrGLIS8D3H1LK5pHapi5iNQ/vXv3LjMt7fLLL6dNmzb85z//4aWXXir3nokTJzJhwgTHz+np6c4vqFoy1Dz9GORn4+nmRduGfmw9msbGw6eUeIuISLmqvKq5SEVl/G8iFsPOgqKeLExvQoS/B2/c3pmvHuhNv5ahZy8J1nMc9PureTxwsrlGtrM1aGsORyzKg70/medUWE1E6ohLKbZ6JldXV7p06XLOQqpQQ2u6eAWBpzm8vKTXWwXWRETkQpR4S41xIjOPGR9/gO/RZeQbNv5l3MGjV7dg8V+uZGjnSCznG5591SSYeAy63V1t8Z6XxVK6pveu7yAvE1L2mD8r8RaRWu6PFFstUVRUxLZt285ZSLVGO62yOUDX4vW8VWBNRETOxQlrLYmUZbcbzFl/hH/8uIO59ulghZVBw/n4rtsrNmTP3afKYrwkbYfCymmwbxEcWQOGHXwbgu/F9QaJiNRkEyZM4K677qJ79+707NmT6dOnn1VsNTIykqlTpwLw4osvctlll9G8eXNSU1N59dVXOXz4MPfdd58z38alCY6BYxsclc27FSfeO4+nk5NfhKebzZnRiYhIDaTEW5xqX2IGE+dtY8PhU4y0LaG16xEK3fy5atw/wKuWz5OL6GQuhZYaB8tfM8+pt1tE6oiKFls9deoU48aNIyEhgcDAQLp168aqVato27ats97CpXNUNjcT74b+HoT5uZOYnsfWo6n0ahbsxOBERKQmUuItTpFXWMTbS/bz7tJYCooMItxymOwxH/LBZcDT5hy62q5kuPnqtyCueHkdJd4iUodUpNjqv/71L/71r39VQ1TVILSVuU/cBpjrk3drEsgP2xLYGHdKibeIiJxFc7yl2q09cILr3ljBvxfvo6DIYEgLT5aFv4FXfgoENYMetXDY4bm0vbnsz5FKvEVEar3IbuY+cQcU5AAqsCYiIuenHm+pNmnZBUz9cRdz1h8BINTXnZcHR3HNxv/DkrQVvELg9tng4ubkSCtRZDdzXnfGcfPnCCXeIiK1nl8k+IRBZiIkbIOonqcVWEvFMIzzFwQVEZF6Rz3eUi0S0nK57o3ljqT7jl6N+eXhLly76UEsx38Hr2C46zto0MbJkVYyq9Vc0xvM+d7eGn4oIlLrWSylvd7HNgLQrqEfbi5WTmblc+hEthODExGRmkiJt1S53IIi/u/TDRxPy6VJsBdfPtCbV65rgv9XI80/WDwDYcy3ENbO2aFWjW5jzffY6Q5nRyIiIpUlsqu5L0683V1sdIj0BzTcXEREzqbEW6qUYRg8M28bW46mEeDlyqf39KJHhCvMGgFH14NHgJl0h3dwdqhVp0FreOoQDJjo7EhERKSynNHjDaXLim3Uet4iInIGJd5SpT5ceZB5vx/DZrXw9h1daexjh1m3wpG14OEPY74xl90SERGpTUpWqTh5ALJPAtC1cQCgHm8RETmbEm+pMsv2JvPKD7sAmHRDG65o7AmzR5pLa7n7w53ztbyWiIjUTp6Bpet5H98ElFY235OYQUZugbMiExGRGkiJt1SJgylZPDJ7E3YDbuveiLt7N4Y5o+HwSnDzhTvnlQ7TExERqY0cw83NxLuBnwdRQZ4YBmw+kuq8uEREpMZR4i2VLiO3gHGfbCA9t5AujQN46eb2WI6uhwNLwMUT/vQ1NOru7DBFRET+mHLmeZeu553qhIBERKSmUuItlcpuN3hszmZikzIJ9/PgP3/qhruLDXZ8YzZoMwQa93JqjCIiIpXi9MTbMAAVWBMRkfIp8ZZKNW3RXhbvTsLNxcp/7uxGAz8PsNth57dmg3Y3OzU+ERGRShPWHqyukJUMaUeA0h7v3+NOYbcbzoxORERqECXeUmm+33qct5bEAvCP4R3oFBVgXji6HjKOm3O7Y652XoAiIiKVydUDwtubx8XDzVuH++LpaiMjt5DY5EwnBiciIjWJEm+pFGsOnGDCF1sAuL9fM4Z1aVR6cec35r7VYPOPFBERkbqiZLj50Q0AuNisdC7+4nmjlhUTEZFiSrzlD9t+LI37Pt5AfqGdgW3CeGpw69KLpw8zb3uzU+ITERGpMmdUNgfoHm0ON1+1/4QzIhIRkRpIibf8IfuTM7lrxjoy8wrp1TSIt+7ogs1qKW1wbAOkHwM3H2iuYeYiIlLHlCTe8ZuhqBCAfi1DAVixL5kizfMWERGUeEtFHF4Fv/4NCnIBOJ6aw50frOVEVj7tI/344K7ueLjayt5TUs285WBw9azeeEVERKpacAuzhklBNiTvBqBLVAB+Hi6kZhdoPW8REQGUeMvFOnUYZo+E5a/C6jc5mZXPnR+u5XhaLs1Cvfl4bE98PVzL3qNq5iIiUtdZrRDZxTwuLrDmYrPSt7jXe9meJGdFJiIiNYgSb7kwexHMfwDy0gEwVr3JQx8uYX9yFhH+Hnx6by+CfdzPvu/YRkg/WjzMfGA1By0iIlJNTl/Pu1j/4sR76d5kZ0QkIiI1jBJvubDfpkPcKnDzwR7YFEtuGr0S5xLk7can9/YiMuAcQ8hLqpm3HKRh5iIiUneVU2DtylZm4r31aBopmXnOiEpERGoQJd5yfsc2wZJXACga/A/+63onAPe5/MCnd7SkeQOf8u8zDFUzFxGR+qEk8U7aCflZADTw9aBdQz8AlqvXW0Sk3lPiLeeWnwXzxoG9ENoO5eWjXfhHXEt2G43xteTQ7vCn57732EZIOwKu3tDimuqLWUREpLr5NQTfCDCKIH6r43T/4l7vpXuUeIuI1HdKvOXcfp4EJ2LBN4JjV7zCJ2sOY2Al+/Inzetr3oXsk+Xfu2O+uW+lauYiIlIPlDfPu1UDAJZrWTERkXpPibeUb89C2DDDPL75Xd5Zd4pCu0Gf5iF0vfZPEN4R8jNh1b/PvtcwYOd35rGGmYuISH0Q2dXcn5Z4n76s2Jajqc6JS0REagQl3nK2zCT49mHzuPd44kMu48sNRwF45KrmYLHAgGfN62v/A5lnDKE7tgnS4jTMXERE6o9yerxdbFb6tigebr5by4qJiNRnSrylLMOAb8dDdgo0aAdXPcd/lh0gv8hOr6ZB9GoWbLZrOQgadoWCbLPq+el2zi9to2HmIiJSHzQsXss79TBkpThOl1Q317JiIiL1mxJvKWvDh7DvJ7C5w/D3ScqB2eviAPjz1S1K253e673+A8hIMI8NA3YUVzNvd3P1xS0iIuJMHv4Q0tI8Pm1ZsZL1vLWsmIhI/abEW8xk+eRB2DIXfppknhs4BcLa8d/lB8gvtNO1cQCXxwSXva/51dCoJxTmwsrp5rnjJcPMvaC5hpmLiEg9Us5w8wZ+HrSN0LJiIiL1nRLv+sYwIDXOXGP7lynwyVD4RzT8uzPMvx8Kc6DZAOj1ACmZeXy29jBg9nZbLJayz7JYYMAz5vGGGZB+HHZ8Y/7cchC4eVXTmxIREakBykm8AQa01rJiIiL1nYuzA5BqFL8VZo+EjONnX7O5QVh7iOoFVz4JVisfrDhIboGdjo38ubJ4qNxZmvWHxpdD3CpY8Trs+9k8r2rmIiJS35xe2dwwzC+oMZcVe3vJfseyYjar5TwPERGRukiJd32y5wcz6ba6QFg7iOhsFoNp2AUatAUXN0fTU1n5fLr6EAB/vqqc3u4SJb3eH98I6z8EDHOYeYtrq/ztiIiI1Chh7c0vsnNOwqlDENQUOHtZsa6NA50bp4iIVDsNNa9PMhPNfd8n4P+Ww03/hu5joWHnMkk3wIzfDpKVX0TbCD+ubtPg/M9t2hei+wKG+XOLazXMXERE6h8XdwjvYB6fa1kxDTcXEamXlHjXJ5nFa4j6nD+RTsspYOZvhwD489XNz93bfbqSud6gauYiIlJ/OeZ5bypzumRZsWV7tJ63iEh9pKHm9UlJj7dP2HmbzfztEBl5hbQK8+XatuEX9+wml8NlD8HJA9Dyuj8YqIiISC11jgJrjmXFjqVxIjOPYB/36o5MREScSD3e9UnGhRPvjNwCZvx2EIDxVzXHWpECMIOnwh1zwdXjj0QpIiJSe5Uk3vFboKjAcbpkWTHDgOX7NNxcRKS+uaTE++233yY6OhoPDw969erFunXrztm2oKCAF198kZiYGDw8POjUqRMLFy78Q8+US2AYp/V4n3uo+SerD5OWU0CzUG+u7xBRTcGJiIjUEUEx4O5vLs+ZtKvMpf6tNM9bRKS+qnDiPXfuXCZMmMDkyZPZtGkTnTp1YtCgQSQllT9nadKkSfznP//hzTffZOfOnTzwwAMMGzaM33///ZKfKZcgNw2K8szjc/R4Z+cX8uHK4t7uAc213ImIiEhFWa0Q2cU8PrahzKX+rcwvvpfvNZcVExGR+qPCife0adMYN24cY8eOpW3btrz33nt4eXkxY8aMctt/+umnPPPMM1x//fU0a9aMBx98kOuvv57XX3/9kp8pl6CksJqH/zmHgn+y+jAns/JpEuzFTZ0aVmNwIiIidUjj3uZ+94Iyp7s2DsDXw4VT2QVsPZpa/XGJiIjTVCjxzs/PZ+PGjQwcOLD0AVYrAwcOZPXq1eXek5eXh4dH2UTP09OTlStX/qFnpqenl9nkAi5QWC0pI5e3fo0FzN5uF5um/4uIyPld6jSxOXPmYLFYuPnmm6s2QGfpeJu5j10MqXGO0+ayYiGAhpuLiNQ3FcquUlJSKCoqIiysbPIWFhZGQkJCufcMGjSIadOmsW/fPux2O4sWLWLevHnEx8df8jOnTp2Kv7+/Y4uKiqrI26ifLpB4//3H3WTmFdKpkT/DuzaqxsBERKQ2utRpYocOHeKJJ56gb9++1RSpEwQ1g6ZXAgb8/lmZS/1bmsPNl+5V4i0iUp9UebfmG2+8QYsWLWjdujVubm6MHz+esWPHYrVe+ktPnDiRtLQ0x3bkyJFKjLiOOk9htY2HTzJv0zEAXhjavmKVzEVEpF66lGliRUVFjB49mhdeeIFmzZpVY7RO0HWMuf/9M7AXOU6XrOe99WgqJzLznBGZiIg4QYWy35CQEGw2G4mJiWXOJyYmEh5e/nrPoaGhfPPNN2RlZXH48GF2796Nj4+P4wP3Up7p7u6On59fmU0u4Bw93kV2g+e/3QHAyO5RdI4KqObARESktrmUaWIAL774Ig0aNODee++9qNep1VPL2gwBzyBIP2YOOS8WdtqyYks03FxEpN6oUOLt5uZGt27dWLy49APEbrezePFievfufd57PTw8iIyMpLCwkK+//pqhQ4f+4WdKBZQUVzujx/vzdXHsOJ6On4cLTw5u5YTARESktrmUaWIrV67kww8/5P3337/o16nVU8tc3KHTKPN408dlLg1qZ3Ys/G/L8eqOSkREnKTC470nTJjA+++/z8cff8yuXbt48MEHycrKYuzYsQCMGTOGiRMnOtqvXbuWefPmceDAAVasWMHgwYOx2+08+eSTF/1MqQSOHu/SUQSnsvJ57ec9APzl2lYE+7g7IzIREanjMjIyuPPOO3n//fcJCQm56Ptq/dSykuHme36EjNIvJG7qbK4csjI2hRQNNxcRqRdcKnrDyJEjSU5O5vnnnychIYHOnTuzcOFCx7fecXFxZeZv5+bmMmnSJA4cOICPjw/XX389n376KQEBARf9TKkE5fR4v/bzHlKzC2gd7svoXo2dFJiIiNQ2FZ0mtn//fg4dOsSQIUMc5+x2OwAuLi7s2bOHmJiYs+5zd3fH3b0WfyncoDVE9YIja2HzLOj7FwCahnjTqZE/W46m8cO2eMb0jnZunCIiUuUshmEYzg7ij0pPT8ff35+0tDTN9z6XV5tDVjI88BuEt2f7sTSGvLUSw4C5919Gr2bBzo5QRKROqeufTb169aJnz568+eabgJlIN27cmPHjx/P000+XaZubm0tsbGyZc5MmTSIjI4M33niDli1b4ubmdsHXrJW/099nwbcPQWBTeGQTFHdOfLjyIC99v5NuTQL5+sHLnRykiIhciop8Lmmx5vqgqBCyUsxjnzDsdoPnv92OYcDQzg2VdIuISIVVZOqZh4cH7du3L7MFBATg6+tL+/btLyrprrXa3QzufnDqIBxa4Tg9pGMEFgtsPHyKIyeznRefiIhUCyXe9UFWMmCAxQZeQcz//Rib4lLxdrPxzPVtnB2diIjUQiNHjuS1117j+eefp3PnzmzevPmsqWfx8fFOjrIGcPOGDreax6cVWWvg58HlMeYX3//bqiJrIiJ1XYXneEstVFJYzTuU9Hw7U3/cDcCfr25BmJ+HEwMTEZHabPz48YwfP77ca0uXLj3vvTNnzqz8gGqqrmNgw4ew63+QdQK8zYT7pk4N+S32BN9tPs5D/Zs7OUgREalK6vGuD04rrPbGL/tIycyjWag3Y69o6ty4RERE6oOGnSGiExTlw9a5jtOD20XgZrOyOyGD3Qm1aI1yERGpMCXe9UFxj3eWWwgzVx0CYMqQdri56B+/iIhIteh6l7nf9DEU17X193Klf6tQAL7brOHmIiJ1mTKv+qA48d6e7kGR3eCatmH0axnq5KBERETqkQ63gqsXJO+GI+scp4d2jgTg283HqQMLzYiIyDko8a4PihPvTSfNqrEP9j97rVQRERGpQh5+0G6YebzpE8fpq9s0wNvNxrHUHDbFnXJScCIiUtWUeNcHxYl3QpE/bSP86BIV4Nx4RERE6qOS4eY75kGuOafbw9XGoPbhgIabi4jUZUq86wEjwyyulmz4c2fvJlgsFidHJCIiUg9F9YTQ1lCQDdu+dJy+qVNDAL7fGk9hkd1Z0YmISBVS4l0P5Kaa36BnuQYztHNDJ0cjIiJST1ksZYusFbuieQjB3m6cyMrnt/0nzv8Mux3mPwjfPOwo0iYiIjWfEu96wFK8nFi3dm3wctPS7SIiIk7TcSTY3CB+CxzbBICrzcoNHSMA+HbzsfPfv2MebJkNmz+DdA1NFxGpLZR413EJySl4GDkA3HBFJydHIyIiUs95B5cWWVvysuN0yYi0n7YnkFtQVP69RYWwdGrpz2lHqypKERGpZEq867gfVm0GINfiQUxkuHODEREREej/NFhdIfYXiF0MQNfGgTQK9CQrv4jFu5LKv2/L53AitvTntCPVEKyIiFQGJd51WEGRnVVbdwFQ5NXAydGIiIgIAEHNoOf95vHPz4G9CIvF4iiyVu5w88I8WPYP89jVy9ynxlVDsCIiUhmUeNdhP+9IxDUnGQDPoAgnRyMiIiIO/Z4AjwBI2gGbZwEwtHMkAEv3JJOWU1C2/caZZg+3bwR0v8c8px5vEZFaQ4l3HfbpmkOEWlIBsPqEOTcYERERKeUVBFc+ZR7/+jfIy6RVuC+twnzJL7Lz0/aE0rb5WbD8NfO43xMQ0tI81hxvEZFaQ4l3HRWblMGaAydpYEkzT/hqfreIiEiN0uM+CGwKmYmw6t8A3FRcZO3bLacNN1/3X8hKgoAm0GUMBESZ51PV4y0iUlso8a6jPltjzvvqFJhnnvDRHG8REZEaxcUNrnnBPP7t35B+3DHPe9X+EySm50JuGqycbrbpP9G8x7848U47orW8RURqCSXedVB2fiFfbzSHn7X1NZcSQ0PNRUREap42N0HUZVCYA7++TFSQF92aBGIY8P3WeFj1FuSmQkgr6HibeY9/I3Ofnwk5p5wWuoiIXDwl3nXQt5uPk5FXSHSwF0FG8QeyEm8REZGax2KBQcXreW+eBfFbHWt6L920E9a8Y14b8AxYbeaxqyd4h5rHmuctIlIrKPGuYwzD4NPVhwEY3asJlszitUA11FxERKRmatQd2g8HDPh5Ete3D8dmtdA3aZbZqx3e0ewZP93pw81FRKTGU+Jdx/x+JJWd8em4u1gZ0bUhOBJv9XiLiIjUWFdPBpsbHFxGSPxyboyGMbafi689D9Yz/mQrGW6uAmsiIrWCEu865rM1Zm/3jR0bEmjJBKMIsJQOSRMREZGaJ7AJ9HrAPP55Eo+5fIWHpYBttjYYMVef3T6gsblXj7eISK2gxLsOOZWVbxZiAe7s3cRcngTAKxhsrk6MTERERC6o71/AMwhS9tA07msA/pY9gp0JGWe3LRlqnhpXjQGKiMilUuJdh/y8M4H8QjttI/zo1MgfMhLMCxpmLiIiUvN5BphLhhXb5dWNtUYbvtt8/Oy2JWt5q7iaiEitoMS7Dlm2NxmAa9uFYbFYTpvfrcJqIiIitUL3sdCgLVhdOXWZmYR/t+U4dvsZ63WXzPHWUHMRkVpBiXcdUVhkZ8W+FACubFk8n7tkqLl6vEVERGoHmyuM/RHGr6dr76vwdXchPi2XDYfPWK+7ZKh5VjIU5FR/nCIiUiFKvOuILUdTycgtJMDLlY6NAsyT6vEWERGpfTwDIKgpHq42BrcPB+DbzcfOaBMIbj7msYabi4jUeEq864hle8xh5n2ah2CzWsyT6vEWERGp1YZ2jgRgwbZ48gvtpRcsFq3lLSJSiyjxriNK5nc7hplDaeLtG+6EiEREROSP6h0TTIiPO6nZBayMTS57saTAmtbyFhGp8ZR41wEnMvPYeiwNOEfiraHmIiIitZLNauHGjhEAZ1c3V4E1EZFaQ4l3HbAyNgXDgDYRfjTw8yi9oKHmIiIitd7Qzg0B+HlnItn5haUX/NXjLSJSWyjxrgNKhpn3axlSerIgF3LNXnD1eIuIiNRenaMCaBzkRXZ+Eb/sSiq9ENDY3Ku4mohIjafEu5az2w2W7z1jGTGArOIPZpsbeARUf2AiIiJSKSwWCzd1Mnu9yww3dxRXi3NCVCIiUhFKvGu5nfHppGTm4eVmo3uToNILjqXEwszKpyIiIlJrlQw3X7Y3idTsfPNkyRzv9ONgL3JSZCIicjGUeNdyJcPML48Jwc3ltH+cmt8tIiJSZ7QI86V1uC8FRQY/bk8wT/qGg9UF7IWQEe/cAEVE5LyUeNdyyx3LiIWUvZBR/KGsxFtERKROKFnT2zHc3GoDP/OcCqyJiNRsSrxrsYzcAjYePgXAlS3PKKDmGGquwmoiIiJ1wZBO5rJiaw6eICEt1zypAmsiIrXCJSXeb7/9NtHR0Xh4eNCrVy/WrVt33vbTp0+nVatWeHp6EhUVxeOPP05ubq7j+pQpU7BYLGW21q1bX0po9cqq/ScotBs0DfGmcbBX2Ysaai4iIlKnNAr0onuTQAwDvt9a3OvtWMtbBdZERGqyCifec+fOZcKECUyePJlNmzbRqVMnBg0aRFJSUrntZ8+ezdNPP83kyZPZtWsXH374IXPnzuWZZ54p065du3bEx8c7tpUrV17aO6pHljmGmYeefVE93iIiInVOSZG177aUJN5ay1tEpDaocOI9bdo0xo0bx9ixY2nbti3vvfceXl5ezJgxo9z2q1at4oorruCOO+4gOjqaa6+9llGjRp3VS+7i4kJ4eLhjCwkJKfd5YjIMg2V7ylm/u4R6vEVEpIpVZATcvHnz6N69OwEBAXh7e9O5c2c+/fTTaoy2bri+QwQ2q4WtR9OITcqEgJIlxZR4i4jUZBVKvPPz89m4cSMDBw4sfYDVysCBA1m9enW591x++eVs3LjR8WF84MABfvjhB66//voy7fbt20fDhg1p1qwZo0ePJi5OQ6bO50BKFsdSc3CzWbmsWfDZDUp6vH3DqzcwERGpFyo6Ai4oKIhnn32W1atXs3XrVsaOHcvYsWP56aefqjny2i3Yx53+xSPd5m06etpa3prjLSJSk1Uo8U5JSaGoqIiwsLK9qGFhYSQkJJR7zx133MGLL75Inz59cHV1JSYmhv79+5cZat6rVy9mzpzJwoULeffddzl48CB9+/YlIyOj3Gfm5eWRnp5eZqtvSnq7ezYNwsvNpexFw4DMkqrmGmouIiKVr6Ij4Pr378+wYcNo06YNMTExPProo3Ts2FFTyy7BLV3Ned3zfz9GkV/xHO/UI+bnv4iI1EhVXtV86dKlvPLKK7zzzjts2rSJefPmsWDBAl566SVHm+uuu45bb72Vjh07MmjQIH744QdSU1P54osvyn3m1KlT8ff3d2xRUVFV/TZqnPPO785NhaJ889hbibeIiFSuSxkBdzrDMFi8eDF79uyhX79+VRlqnXR1mwb4ebgQn5bL2pPFxVULsiDnlHMDExGRc6pQ4h0SEoLNZiMxMbHM+cTERMLDyx/S/Nxzz3HnnXdy33330aFDB4YNG8Yrr7zC1KlTsdvt5d4TEBBAy5YtiY2NLff6xIkTSUtLc2xHjtSveU25BUWsOXACgH7nK6zm4Q+uHtUYmYiI1AeXMgIOIC0tDR8fH9zc3Ljhhht48803ueaaa87ZXiPcyufhamNIJ7PI2lebk0u/ZE/VND0RkZqqQom3m5sb3bp1Y/HixY5zdrudxYsX07t373Lvyc7Oxmot+zI2mw0wv/EuT2ZmJvv37yciIqLc6+7u7vj5+ZXZ6pN1B0+SV2gn3M+DlmE+ZzdQYTUREamBfH192bx5M+vXr+fll19mwoQJLF269JztNcLt3EqGm/+4PaF0uLnmeYuI1FguF25S1oQJE7jrrrvo3r07PXv2ZPr06WRlZTF27FgAxowZQ2RkJFOnTgVgyJAhTJs2jS5dutCrVy9iY2N57rnnGDJkiCMBf+KJJxgyZAhNmjTh+PHjTJ48GZvNxqhRoyrxrdYdpw8zt1gsZzdwLCWmxFtERCrfpYyAA3M4evPmzQHo3Lkzu3btYurUqfTv37/c9hMnTmTChAmOn9PT05V8F+vaOICmId4cTMkinhAagSqbi4jUYBVOvEeOHElycjLPP/88CQkJdO7cmYULFzqGm8XFxZXp4Z40aRIWi4VJkyZx7NgxQkNDGTJkCC+//LKjzdGjRxk1ahQnTpwgNDSUPn36sGbNGkJDyxlGLaWJd6tz/H7U4y0iIlXo9BFwN998M1A6Am78+PEX/Ry73U5eXt45r7u7u+Pu7v5Hw62TLBYLt3SJ5PVFe9mc4Wsm3lrLW0Skxqpw4g0wfvz4c36wnjlkzMXFhcmTJzN58uRzPm/OnDmXEkb9Y7dzcsMX+CXHY7W05IqYc6x1nlFS0VyJt4iIVI2KjoCbOnUq3bt3JyYmhry8PH744Qc+/fRT3n33XWe+jVptWFcz8d5wyocbXYE0zfEWEampLinxFifIz4L5DxC06ztmubnxYINP8PdyLb+tY6i5KpqLiEjVqOgIuKysLB566CGOHj2Kp6cnrVu35rPPPmPkyJHOegu1XqNALy5rFsTRQ8VfxKvHW0SkxlLiXRukH4fPb4f4LQB4WvK532sZcF357TXUXEREqkFFRsD97W9/429/+1s1RFW/DO/aiI8OBgNgpB2lnMovIiJSA1T5Ot7yBx3/Hd6/CuK3YHgF86lxPQA9kr+Gwvzy71GPt4iISL1wXYcITriYX7RbslMgP9vJEYmISHmUeNdkO76BGddBRjyEtmbr4Hm8mHc7yQTimp0EO+aVf596vEVEROoFH3cXLm8XQ4bhaZ7QkmIiIjWSEu+ayDBg+avw5V1QmAPNB8K9P/NLgicFuLAmZLjZbvVbZtvTFRVA9gnz2PfcS7qIiIhI3TC8WxTHDHOed/7JQ84NRkREyqXEu6YpyIV598OvxfPgej0Io+aChz/Li5cRs3e9G1w8IWEbHFpZ9v6sFMAAiw08g6o1dBEREal+vWOCOWEzlxjds2eXk6MREZHyKPGuaeaOhm1fmInzjf+C6/4ONhdOZuWz9VgaAJe1bwGdR5nt17xT9v7MkqXEGoBV/3hFRETqOpvVgmeDpgAc3r/bydGIiEh5lJnVJCcPQuwvYHWBO+dB93scl1bGpmAY0DrclzA/D7jsIfPCnh/hxP7SZ6iwmoiISL3TuGlLAApPxpGSmefkaERE5ExKvGuSA0vMfVQvaNa/zKWSYeZ9WxSv1RnSAloMAgxY825pQxVWExERqXdCIpsDEGFJ4bvNx50cjYiInEmJd02y/1dzHzOgzGnDMFixz0y8+7UMLb3Qu7jXe/MsyDllHjsSb/V4i4iI1BsBjQGItKTw9SZVNhcRqWmUeNcURYVwYLl53OyqMpf2JmaSmJ6Hh6uVHtGnFUxreiWEtYeCbNg40zznGGquHm8REZF6w78RAOGcZPfxU+xOSHdyQCIicjol3jXF8d8hLw08AqBh5zKXSoaZ92oajIerrfSCxVI613vtf82lxBw93lpKTEREpN7wCQerKy4WO2GcYt6mY86OSERETqPEu6YoGWberD9YbWUuLS9vmHmJDiPAuwFkHIcd30CGhpqLiIjUO1Yr+EcC5nDzz9fFcSA508lBiYhICSXeNcU55nfn5Bex9uBJAK5sGXL2fS7u0HOcebzm7dOWE9NQcxERkXrFPwqAK0Kzycgt5L5PNpCeW+DkoEREBJR41wy56XB0vXncrGzivfbgCfIL7UT4exAT6lP+/d3vAZu7OVz91CHznHq8RURE6pfixPu+jq5E+HtwIDmLP3/+O0V2w8mBiYiIEu+a4NAKMIogKAYCm5S5tGJfCgD9WoRisVjKv987BDrdXvacerxFRETqlwAz8fbJief9Md3xcLWydE8y/1y428mBiYiIEu+awDHM/KqzLpUUVit3fvfpSoqsAbh6g/s5esdFRESkbiru8Sb1CO0j/Xl1RCcA/rP8APO0xJiIiFMp8a4J9i8x92ck3sdTc9iXlInVAn2alzO/+3QNWkPzgeaxr3q7RURE6p3iHm/SjgAwpFNDxg9oDsDT87bxe9wpZ0UmIlLvKfF2tlOH4eR+sNgguk+ZSyuKq5l3igrA38v1ws/q87j5nIZdqiJSERERqclKerzTjoJhzuuecE1LrmkbRn6hnf/7dCMJablODFBEpP5S4u1sB4p7u6N6godfmUvL95rzu/u2uMAw8xLRfeDPv8NNb1VmhCIiIlIb+JnLiVGQDdnmiihWq4V/jexMqzBfkjLyuP/TDeQWFDkxSBGR+kmJt7M51u8uW828yG6wMtZMvMtdRuxcApuAm1dlRSciIiK1hatHaXHVtDjHaR93Fz64qzuBXq5sPZrGU19vxTBU6VxEpDop8XYmexEcWGYenzG/e+vRVNJyCvD1cKFTo4Dqj01ERERqn9MKrJ0uKsiLd0Z3w8Vq4dvNx/lw5UEnBCciUn8p8Xam45shNxU8/M+al10yzLxP8xBcbPrHJCIiIhfBv5G5Tzu7innvmGCeH9IWgH8v3kdGbkF1RiYiUq8po3OmkmHmTfuBzaXMpeX7LnIZMREREZESZ1Q2P9OfejUhJtSb9NxCPl8XV24bERGpfEq8nekc63en5RSw+UgqAH1bVGB+t4iIiNRv/o3NfWr5SbXVauGBK2MA+GDFQfIKK1hoLfUIrJwOBTl/IEgRkfpHibez5GXA0XXm8RmF1VbFplBkN2gW6k2jQBVKExERkYtU0uOdevicTYZ2jiTC34OkjDzmbzp28c82DPhqLPwyGda+9wcDFRGpX5R4O8uhlWAvhMCmENS0zKXl+8z53f0udhkxEREREYCQluY+YRv8+rJjPe/TublYua9vMwD+s/wARfaLrHB+cDkcXW8e7/2pMqIVEak3lHg7yzmGmRuGwfK95vzuKzW/W0RERCoiOAaummQeL/8nLPiLuYrKGW7vEUWAlysHU7JYuD3h4p694rXS4yNrIedUJQQsIlI/KPF2lv1LzH1M2WHmB1KyOJaag5vNSq9mQU4ITERERGq1fn+FG14HLLDhQ/j6XijML9PE292Fu3pHA/DustgLr+t9ZL3Z4211Ab9GYNghdnHVxC8iUgcp8XaG1Dg4sQ8sNojuW+ZSSW93j6aBeLm5lHe3iIiIyPn1uA9GzACrK+yYD5+PhLzMMk3uujwaT1cb24+lszI25fzPW/G6ue90O7S/xTzet6gKAhcRqZuUeDtDSW93ZDfwDChzqSTx7qv53SIiIvJHtL8FRn8Brt7mFLdPhkL2ScflIG83bu9pFmN7d+n+cz8nYTvs/REsVugzAVoOMs/HLgK7vSrfgYhInaHE2xkOlAwzLzu/u7DIzpoD5geiCquJiIjIHxZzFdz1HXgGwrENMGMwpJVWMr+vbzNcrBZW7T/hWMr0LCW93W1vNueQR/UCdz/IPgHHf6/ytyAiUhco8a5u9iI4sNQ8PiPx3peUSU5BEb7uLrQO963+2ERERKTuadQd7vkJ/CIhZQ/MGAQp+wCIDPBkaOdIAN4rr9c7JdYcqg7Q9y/m3uZaWqNm389VHb2ISJ2gxLu6xW82q4C6+5lDzU+z7WgaAO0j/bFaLU4ITkREROqk0FZm8h3cAtKOwMwb4eQBAB640lxa7KedCcQmlZ0Hzsp/AQa0vA7C25eeb3GtuVfiLSJyUZR4V7eS+d1N+4GtbPG0LUdTAejYyL+agxIREZE6LyAK7lkIDdpBZgJ8PBRSj9AizJdr2oZhGPDf5af1eqfGwdY55nG/J8o+q/lAc398E2QmVU/8IiK1mBLv6uYYZj7grEvbjpk93h2UeIuIiEhV8A6BMd9AcHNIizMLrmUk8mD/GADm/36M+LQcs+1v/wZ7ITS90hyufjrfcIjoZB5rWTERkQtS4l3dkveY+zOGmecVFrErPh2ATo0CqjkoERERqTd8GsCYb8G/MZzcD5/eTNcQg15NgygoMvhwxUHISIRNn5jtS+Z2n8kx3Pyn6olbRKQWU+JdnQpyIat4OJZ/4zKX9iRkUFBkEODlSqNATycEJyIiIvWGfyO461vwjYCknfDpMMZf3gCA2eviyF3xJhTlQaMe5vS48rQoWVbsVygqrKbARURqJyXe1Sm9ePkOF0/wCipzaWtxYbUOkf5YLCqsJiIiIlUsqJnZ8+0VDPGb6bPuITqHu+Gan4Zlwwdmm75PwLn+LonsCp5BkJcGR9dVX9wiIrXQJSXeb7/9NtHR0Xh4eNCrVy/WrTv//2ynT59Oq1at8PT0JCoqiscff5zc3Nw/9MxaKe2IufdvdNaHWElFcw0zFxGR2qIin93vv/8+ffv2JTAwkMDAQAYOHFg3P+trm9BWcOc34OGP5cgaZrhP4/9cvsfdnsMJ7xbQctC577XaSousqbq5iMh5VTjxnjt3LhMmTGDy5Mls2rSJTp06MWjQIJKSyq9oOXv2bJ5++mkmT57Mrl27+PDDD5k7dy7PPPPMJT+z1ko7au4Dos66tFWF1UREpBap6Gf30qVLGTVqFEuWLGH16tVERUVx7bXXcuzYsWqOXM4S0RFGfw1uPgQlruIhl+8AmHxqMPN+v8A/H8c870VVHKSISO1W4cR72rRpjBs3jrFjx9K2bVvee+89vLy8mDFjRrntV61axRVXXMEdd9xBdHQ01157LaNGjSrzLXdFn1lrlSTe/o3KnM7JL2JvYgagpcRERKR2qOhn96xZs3jooYfo3LkzrVu35oMPPsBut7N4sSpi1whRPWDUHHDxAOCER2N+sPfiya+2snTPeTpCml8NWCBxO6TpSxQRkXOpUOKdn5/Pxo0bGThwYOkDrFYGDhzI6tWry73n8ssvZ+PGjY5E+8CBA/zwww9cf/31l/zMvLw80tPTy2y1gmOoedke753x6RTZDUJ83An383BCYCIiIhfvUj67z5SdnU1BQQFBQUEXbizVo2lfuH02NOxC4C3/4qbOjSi0Gzw0axNbjqSWf49XkFmADSBWvd4iIudSocQ7JSWFoqIiwsLCypwPCwsjISGh3HvuuOMOXnzxRfr06YOrqysxMTH079/fMdT8Up45depU/P39HVtU1NlDt2uk1PIT721HUwHo1EiF1UREpOa7lM/uMz311FM0bNiwTPJ+plr7RXtt1vxquH8p1pYD+eeITvRtEUJ2fhFjZ67nYEpW+feUDDffq3neIiLnUuVVzZcuXcorr7zCO++8w6ZNm5g3bx4LFizgpZdeuuRnTpw4kbS0NMd25MiRSoy4Cp1jqLmjormGmYuISD3w97//nTlz5jB//nw8PM490qvWftFeR7i5WHn3T93oEOnPyax8xsxYS1JG7tkNWxYn3geWQmFetcYoIlJbVCjxDgkJwWazkZiYWOZ8YmIi4eHh5d7z3HPPceedd3LffffRoUMHhg0bxiuvvMLUqVOx2+2X9Ex3d3f8/PzKbDWeYZw78S4urKb53SIiUhtcymd3iddee42///3v/Pzzz3Ts2PG8bWvtF+11iI+7CzPu7kGTYC+OnMzh7hnrycgtKNsovCP4hENBFhxe5ZxARURquAol3m5ubnTr1q1MIZSSwii9e/cu957s7Gys1rIvY7PZADAM45KeWStlpUBRHmABv0jH6cy8QvYnZwLQITLAObGJiIhUwKV+dv/zn//kpZdeYuHChXTv3v2Cr1Mrv2ivg0J93fnknp6E+LixMz6d//t0I3mFRaUNLBZoUbKsmOZ5i4iUp8JDzSdMmMD777/Pxx9/zK5du3jwwQfJyspi7NixAIwZM4aJEyc62g8ZMoR3332XOXPmcPDgQRYtWsRzzz3HkCFDHAn4hZ5ZJ6TFmXvfcHBxc5zefiwNw4CG/h6E+ro7KTgREZGKqejfA//4xz947rnnmDFjBtHR0SQkJJCQkEBmZqaz3oJUQJNgbz66uyfebjZW7T/BX7/cit1ulDZwLCumed4iIuVxqegNI0eOJDk5meeff56EhAQ6d+7MwoULHQVW4uLiyvRwT5o0CYvFwqRJkzh27BihoaEMGTKEl19++aKfWSecY5j5Ns3vFhGRWqiifw+8++675OfnM2LEiDLPmTx5MlOmTKnO0OUSdWjkz3t3dmPsR+v5bstxIvw9mHh9G/Nis/5gdYET++DkAQhq5tRYRURqGothGMaFm9Vs6enp+Pv7k5aWVnOHoa1+G356BtoNg1tnOk4/8vnv/G/Lcf46qBUPD2juvPhERKRS1YrPplpGv9OaYd6mo0z4YgsAk4e0ZewVTc0LM2+EQyvguleh1/1OjFBEpHpU5HOpyquaS7ELLCWmwmoiIiJSG9zStRFPDm4FwIvf7+SHbfHmhRbXmHsNNxcROYsS7+qSdnbinZZdwKET2QB0iFTiLSIiIrXDg1fGcOdlTTAMeGzuZtYdPAktBpkXD62A/GznBigiUsMo8a4u5czx3la8jFiTYC8CvNzKu0tERESkxrFYLEy5qR3Xtg0jv9DOfR+vZ5+9Ifg3hsJcWP2Ws0MUEalRlHhXl5LEO6C0x3vrsVRAvd0iIiJS+9isFv49qgvdmgSSnlvIXR+tJ/Wyv5oXl7wC+5c4N0ARkRpEiXd1yM+G7BTz+LQe761HzB5vze8WERGR2sjD1cYHY7rTLNSb42m53L42mvyOowEDvr4P0o87O0QRkRpBiXd1SD9m7t18wCPAcbpkqHmHyICz7xERERGpBQK93fh4bE9Cfd3ZnZDBfckjKWrQ3ux0+HIsFBU4O8TqUfsXChKRKqTEuzqcXljNYgEgJTOPY6k5WCzQPlJLooiIiEjtFRXkxUd398Dbzcbyg5kMP/EABS4+cGQNLH7B2eFVvcI8eKc3/HcA2O3OjkZEaiAl3tXBsZTY2YXVmoV44+vh6oyoRERERCpN+0h/Pr2vF81CvdmcFcT47HHmhVVvwq7/OTe4qnZkHSTvguObIPWws6MRkRpIiXd1KKeieen87gAnBCQiIiJS+bo2DuTHR/vy+MCWLLH04v3C6wHI++r/KEje7+ToqtChFaXHSTudF4eI1FhKvKtDuUuJpQIqrCYiIiJ1i7uLjUcHtmDhY31ZFvUwG+wtcS/K4vC7I9i0v44WWzu4vPQ4cYfz4hCRGkuJd3UomeMd0NhxautRVTQXERGRuqtZqA+f3n8Fide+x0n8aG4/wJ6PHuK5b7aTX1iH5kHnZ8HR9aU/J253XiwiUmMp8a4OaWXneCek5ZKUkYfNaqFthBJvERERqZssFgs39OmGy60zsGNhlMsSstd9yrhPNpCdX+js8CpH3Gqwn/ZeEjXUXETOpsS7qtntkFa8nFhx4r31aCoALRr44Olmc1JgIiIiItXDr901WAc8A8DfXGewe+8e/vTBWlKz850cWSU4WDy/u/lAc39yP+RnOy8eEamRlHhXtawksBeAxQq+DYHSiuYaZi4iIiL1Rt8nIKoXnpZ8Hvf4H5viUrntP6tJSMt1dmR/TMn87g63glcwGHZI3u3cmESkxlHiXdVKlhLzbQg2FwC2FM/v7qCK5iIiIlJfWK1w1SQAbrMuoYNvBnsTMxn+7ioOpmQ5ObhLlJMK8ZvN46b9IKydeazK5iJyBiXeVe2M+d2GYbCteKh5J/V4i4iISH3StB806YPVns/nrVcRHezFsdQcRry7iu3FIwJrlcOrzB7u4Obg1xDC2pvnVdlcRM6gxLuqlSwlFhAFwNFTOZzKLsDVZqFVuK8TAxMRERFxggETAfDZMZuv72hMu4Z+nMjK5/b/rmH1/hNODq6CSoaZN+1n7hu0NfeqbC4iZ1DiXdXOWMO7ZH5363A/3F1UWE1ERETqmeg+EN0X7AUEb3qTz++/jF5Ng8jMK+Suj9bx5YYjtWe5sUPFhdVKEu+SoeaJO8AwnBOTiNRISryr2hlDzbcUDzPvoGHmIiIiUl8VVzjn98/wy43n43t6ck3bMPIL7fz1q61cNnUxU77bwfZjaRglCaxhwKGVkJnsvLhPl5VS2rMd3dfch7Y2C+pmn4DMJOfFJiI1jhLvquZIvM2h5tuKC6tpfreIiIjUW00uh6ZXmiu/LH8ND1cb747uyqNXtyDU152TWfnMXHWIG99cyXVvrOCDFQfI+vlvMPMGeK8PpMY5+x2U9nY3aAfeIeaxmxcENTOPNdxcRE6jxLuqOYaaR2EYBrvi0wFo11CJt4iIiNRjJb3em2fBqUO42Kw8fk1LVj99FR+N7cENHSNws1nZnZDBgYVv4b36NbN9ZgJ8NgKyTzovdjh7fncJVTYXkXIo8a5KeZmQc8o89m9ESmY+p7ILsFggJtTHubGJiIiIOFPjy6DZALAXwvLXHKddbFYGtGrA23d0Zf2zA5nZO4mXXD8C4KPCQRw3giBlD/mfjYSCHGdFDwfPmN9dQpXNRaQcSryrUklvt7s/ePixLzEDgMZBXni6qbCaiIiI1HOOXu/ZcPLgWZf9kzfQf9vT2LCT1uYO9nebxNiCp0kzvHA7vo7D799BUWFhNQcNpB+HE/vM+dxNLi97TZXNRaQcSryr0hlLie1LygSgRQMtIyYiIiJCVE+IuRqMojK93gAk7YLPR0JhLrS8Dv8Rb/K3YR157aHb+XvAZPIMF5ok/cqPr47h98PVPOy8pLc7ohN4BpS9VjLUPHkPFDnhSwERqZGUeFelMyqa7y3u8W4ZpmHmIiIiIkBpr/eWz+HEfvM47Rh8Nhxy06BRTxgxA2wugLkyzN8e/T/Wdvk7dizcmLeARe8/zTPzt5GanV89MZ9rfjdAQBNw84GifDgRWz3xiEiNp8S7Kp2ReO9LNHu8W4apx1tEREQEgEbdofk1pb3eOadg1ghIPwYhLeGOuWa18NPYrBb63TyO7KteBuBJl7nkrf+Mq15fxje/HytdgqyqHDpP4m21QoM25rGGm4tIMSXeVclR0bwRhmGwN8ns8W6hHm8RERGRUv0nmvutc+CTm82K4D7h8KevwSvonLf59HsYrngUgH+4vU+HnPU8Nncz//fpRpIz8qom1lOHzOXMrC4QdVn5bVTZXETOoMS7Kp22lFhyZh6p2QVYVdFcREREpKxG3aDFIDDsEL8Z3P3MpDug8YXvvXoKdLgNF4p43+PfXO6ym593JnLtv5bx3Zbjld/7XTLMPLI7uJ/jbzpVNheRMyjxrkqOoeZRjmHmTYK98XBVRXMRERGRMvo/DVjA5ga3z4bw9hd3n9UKQ9+GZv1xs+cwy+Ul3vD/nPzsdP78+e88NGsTKZmV2Pt9vvndJRyVzZV4i4hJiXdVsReZS00A+DdyFFZr0UC93SIiIiJniewKd30H9y6Cpn0rdq+LG4z8DDrdgQWDoXn/Y7X/s1xl28KP2xO49l/LWbA1/o/HaBinJd7niTGsOPFOO2IWiBORek+Jd1XJSAB7oTn/xzecvSqsJiIiInJ+TftBw86Xdq+7Lwx7F+6cDwGN8ctLYIbrP5jh+1/ISuHh2Zt4ePYmth5NvfTh5yn7IDMRbO5mtfVz8QwEP7O4Loma5y0iSryrTsn8br+GYLWxL1GF1URERESqXMxV8NAa6D0eLFauKljKbz5PcYvLShZsPc5Nb/3G5X//lee/3c7KfSkUFNkv/tkHl5n7xr3A1eP8bUt6vVXZXERQ4l11TpvfbRjGaWt4q8dbREREpEq5ecOgl+HeX6BBOzwL05jm8g4Lgt4gyi2D+LRcPll9mD99uJZuLy3i8bmb+XFbPFl5hed/7sXM7y6hyuYichoXZwdQZ52WeCdl5JGeW4jNaqFZqLdz4xIRERGpLxp1g/uXwqo3YNk/aZe9jmWhp1jV5yO+P2CwaGciJ7Lymf/7Meb/fgwPVytDOjbkjl6N6RwVgMViKX2W3Q6HVpjH0ReTeKuyuYiUUuJdVU5bw7ukt7tJsBfuLqpoLiIiIlJtXNyg31+h9RD4bDjWE/vos/Ju+tz1PS8P68CmuFP8vCOBn3YkEncymy83HuXLjUdpG+HH6MsaM7RzJD7uLpC0A3JOgau3WQjuQhyVzXeaRdlOT+JFpN7RUPOqUibxNgurqaK5iIiIiJM0aA13fw/+UXAiFmbegC0znh7RQTx7Q1uW/bU/Xz/Ym1u6ROLmYmVnfDrPzt9Or5d/4Zn520jY/LP5nCaXg831wq8X0gKsrpCfAalxVfveRKTGU+JdVVJPX8Nb87tFREREnC6oKdy9APwbw8n98NH1js4Si8VCtyZBTBvZmbUTr2bSDW1oFuJNVn4RX649wP7fvgZgs0vHC88FBzM5D21tHmu4uUi9p8S7qpT0eAdEla7hrcRbRERExLkCm8DYBRDQBE4dhJk3lHaYlDTxduO+vs1Y/OeeLOm7h3U+f+EKm5k8T9wSQq9XFvPs/G1sP3aBNbqLK5vnH9/K5iOppGbnV8lbEpGa75IS77fffpvo6Gg8PDzo1asX69atO2fb/v37Y7FYztpuuOEGR5u77777rOuDBw++lNBqhtw0yDP/R2z4NWSfYw1vDTUXEZG6oyJ/D+zYsYPhw4cTHR2NxWJh+vTp1ReoyJkCGps934HRcOoQzLweTh0uvZ6XASunY3mjI03Xv0BgYTJF3mEsbfU82UFtyMwrZNbaOG58cyU3vbWSz9fFkVncC56RW8CaAyf4YMUBvksIBODnJUu4+e3fuP6NFRw+kVX971dEnK7CxdXmzp3LhAkTeO+99+jVqxfTp09n0KBB7NmzhwYNGpzVft68eeTnl367d+LECTp16sStt95apt3gwYP56KOPHD+7u7tXNLSao6S32zOQhFwXMvLMiuZNQ1TRXERE6oaK/j2QnZ1Ns2bNuPXWW3n88cedELHIGQKi4O4f4OMb4eQBs+d75KewZyGsfQ9yU812/o2hz2PYOo+mv6sHS+wGaw6cYPa6OH7akcDWo2lsPbqNv32/k1Bfdw6dyHa8RD+rPze5QWtLHG42K8fTcrn9v2v4fNxlROvvQpF6pcI93tOmTWPcuHGMHTuWtm3b8t577+Hl5cWMGTPKbR8UFER4eLhjW7RoEV5eXmcl3u7u7mXaBQYGXto7qgkchdWiHIXVolXRXERE6pCK/j3Qo0cPXn31VW6//fba/eW61C3+kWbPd3BzcynY//aHZX83k+7gFnDzu/DnTdDjXnD1AMBqtXB58xDeuqMrayZezTPXt6Zp8VzwkqQ7MsCTQe3C6N+nPwAxtgR++0tvWjTwIT4tl5H/Xc2B5EznvGcRcYoK9Xjn5+ezceNGJk6c6DhntVoZOHAgq1evvqhnfPjhh9x+++14e5f9lm/p0qU0aNCAwMBArrrqKv72t78RHBxckfBqjjQVVhMpYbfby4x6EakrXF1dsdnq5xeqlfH3wMXIy8sjLy/P8XN6enqlPVvEwa+hmXzPvBFO7IOwDtB3ArQdCtbz/zce7OPO/f1iGNe3GZviTpGdX0S7hv4EebuZDQwDtgVhyTlJaO4hPr//Mu54fw17EzO5/b9rmD3uMppr1RuReqFCiXdKSgpFRUWEhYWVOR8WFsbu3bsveP+6devYvn07H374YZnzgwcP5pZbbqFp06bs37+fZ555huuuu47Vq1eX+0dNjf8gLmcNbxVWk/ooPz+fgwcPYrfbnR2KSJUICAggPDwcSz1bn/eP/j1wsaZOncoLL7xQac8TOSffcLh/KaTshYZdKrzmdklF9HIuQFg7OLQCEncQ0rAzn4+7jNEfrGV3Qga3/3cNc+7vRfMG+jtRpK6r8BzvP+LDDz+kQ4cO9OzZs8z522+/3XHcoUMHOnbsSExMDEuXLuXqq68+6zk1/oO4pDJmQBT7DqmwmtRPhmEQHx+PzWYjKioKq1WLKEjdYRgG2dnZJCUlARAREeHkiOqmiRMnMmHCBMfP6enpREVFOTEiqdPcfSCya+U/97TEG8xe8tnFyfeu+HTHnG910ojUbRVKvENCQrDZbCQmJpY5n5iYSHh4+HnvzcrKYs6cObz44osXfJ1mzZoREhJCbGxsuYl3jf8gLu7xNvwaEeuoaK7/mUr9UlhYSHZ2Ng0bNsTLy8vZ4YhUOk9PTwCSkpJo0KBBvRp2/kf+HqgId3d3zQeX2i+snblPKl3LO8jbjdn39WL0B2vZGZ/OHf/5//buPD6mc3/g+GdmkpnsE2RHJIggSFpEQ22lYr241HKpKK3rVvqjqi1ttejCbVWVq3pvG0HvrbRalCqlsbT2NbVvaRCSSCxZZZ05vz8OU5EgiUQSvu/X67xmzv6cJ+HJ9zzbdlZ2z6WOTS4o5kKL2WwmOzefHMUKx8cHoreTvymFqI5KFXjr9XpatmxJdHQ0/fr1A9T+m9HR0YSHh9/13OXLl5Obm8vw4cPveZ8LFy5w5cqVO9YgVPmC+EbgfVnnRkZuKlZaDT61ZORK8WgxmUyA+v+GEA+rmy+V8vPzH6nA+37+HhDikXMz8L50tNDmGvZ6vn6hDZP//R1/v/YxdX46U+zpWsD+xrL851UsdH6Zhq4ONHRzoMHNTzcHHAwPtCGrEKKUSv0vdOLEiYSFhdGqVSuCg4OZO3cuWVlZPPfccwCMGDGC2rVrM3PmzELnRURE0K9fvyIDpmVmZjJ9+nQGDBiAh4cHsbGxvPbaazRs2JDQ0ND7eLRKYsqHjAQATucagVR8XezRW0kzW/FoetT6vopHy6P8+13avwfy8vI4duyY5fvFixeJiYnBwcGBhg0bVtpzCFHhXJsAGshKgcxkcLgx3Z6pAOcDC1iYORONNpd0xZbfzQ1Q0GBGixkNZjQoaNFgpqvuIAO0W4m43J0NKd5sOFa4xUm9Wna8FtqYXi2k64sQVVGpA+/BgweTkpLC22+/TVJSEkFBQaxfv94ywMr58+eL9OU8efIk27ZtY8OGDUWup9PpOHToEEuWLCE1NRUvLy+6devGu+++W7Vrte8kI1FtGqTTczRNnXZCmpkLIYR42JT274GEhAQee+wxy/rs2bOZPXs2HTt2ZMuWLQ86+UI8OHo7qFkfrsaqtd4ObpB8HFa9CAkH0AD59buy0m0iV7Qu1LTXU9PBQE07PTXt9dRy0FPDTo+ychTaoytZ5rOWH5rN50xKJmeSMzmTnMXlzFzOXbnOuK8PsP6oF+/2DcDZTlqcCVGVaBRFUSo7EfcrPT0do9FIWloaTk5OlZuYczsgsgfU8OVVryUs33+BCV39mNC1UeWmS4gHLCcnh7i4OHx9fbGxsans5FQqHx8fJkyYwIQJEyo7KaKc3e33vEqVTQ8JyVNRbX3zLBxfDV2ng7kAtv4TTHlgMEL3mRD0t3uPpH41Dv7VGsz5MPx7aNjVsiv1eh6LtsWxYEssJrOCq6OBfw5ozlON3e9yQSHE/SpNuSTtn8vbrVOJJcvAakJUJxqN5q7LtGnTynTdvXv3MmbMmHJJ47Jly9DpdIwbN65crieEEOIBuNnPO3oGbHpXDbobdYdxu+CxYSWbvqymL7T5u/p9w9tgNll2OdvpmdjNnxX/aEsDV3tSMnIZtXgfr333Oxk5+RXwQEKI0pLAu7ylngdAca7LmRtzeMtUYkJUD4mJiZZl7ty5ODk5Fdo2adIky7GKolBQUFCi67q6upbbyO4RERG89tprLFu2jJycnHK5Zlnl5eVV6v2FEKLauBl4KyawcYb+/4GhUeDkVbrrtH8FbIzqCOkxXxfZHVjXmbX/157nn/RFo4Fv912g+9zf2HHm8l0vm1dg5iFoBCtElSaBd3m7UeOdofcgK8+EtU5DPRnRXIhqwcPDw7IYjUY0Go1l/cSJEzg6OrJu3TpatmyJwWBg27ZtxMbG0rdvX9zd3XFwcKB169b88ssvha7r4+PD3LlzLesajYYvv/yS/v37Y2dnh5+fH6tXr75n+uLi4tixYweTJ0+mUaNGrFixosgxixYtIiAgAIPBgKenZ6ERplNTU/n73/+Ou7s7NjY2NGvWjB9//BGAadOmERQUVOhac+fOxcfHx7I+cuRI+vXrx/vvv4+Xlxf+/v4AfPXVV7Rq1QpHR0c8PDz429/+Zpnf+qajR4/Su3dvnJyccHR0pH379sTGxvLrr79ibW1NUlJSoeMnTJhA+/bt75knQghRLfh2AM8gCOgP43ZD4OCS1XLfzq4mdHhN/b7pPcjLKnKIjbWOt3o35ZsxIXjXtONiajZ/+3I3Y7/az0vLDhK2aA/9P9tOl4+3EPz+LzSeuo5Gb62j3axNrDucKAG4EBVEAu/ylnQIgAsadUTJ+i4OWOskm4VQFIXreQWVspTnHxGTJ09m1qxZHD9+nBYtWpCZmUnPnj2Jjo7m4MGDdO/enT59+nD+/Pm7Xmf69OkMGjSIQ4cO0bNnT4YNG8bVq1fvek5kZCS9evXCaDQyfPhwIiIiCu1fuHAh48aNY8yYMRw+fJjVq1dbRos2m8306NGD7du389///pdjx44xa9asUk+BFR0dzcmTJ9m4caMlaM/Pz+fdd9/l999/Z9WqVZw9e5aRI0dazrl48SIdOnTAYDCwadMm9u/fz6hRoygoKKBDhw7Ur1+fr776ynJ8fn4+//vf/xg1alSp0iaEEFWWjRH+vhWeWQyO9znXffAL4FwPMpNgx7/ufJhvTdaNb8/wJ7wBWH80iTW/J7D1VAoHz6cSm5JFckYuOflmABLScvjH/w4wesk+4q9ev780CiGKkAn/ylP2NUg4CMB+XXPgKn7SzFwIALLzTTR9++dKufexGaHY6cvnv7sZM2bw9NNPW9Zr1qxJYGCgZf3dd99l5cqVrF69+q7zGY8cOZKhQ4cC8MEHHzBv3jz27NlD9+7diz3ebDazePFi5s+fD8CQIUN45ZVXLAN7Abz33nu88sorjB8/3nJe69atAfjll1/Ys2cPx48fp1EjdbDH+vXrl/r57e3t+fLLLwvNz35rgFy/fn3mzZtH69atyczMxMHBgQULFmA0GomKisLa2hrAkgaA0aNHExkZyauvvgrAmjVryMnJYdCgQaVOnxBCPPSsDND1HfhuFGz/FFqOBMfiB1GzN1jxXr/m9Gnhxa4/ruJgY4WTjRVOttY42VjjZGuFk401NtY6lu48y+dbY9l0IpmdsVeY0NWPUU/6SgWSEOVE/iWVp7hf1anEXPyJSVX7c8rAakI8XFq1alVoPTMzk0mTJtGkSROcnZ1xcHDg+PHj96zxbtGiheW7vb09Tk5ORZpn32rjxo1kZWXRs2dPAFxcXHj66adZtGgRAMnJySQkJNClS5diz4+JiaFOnTqFAt6yaN68eaGgG2D//v306dMHb29vHB0d6dixI4AlD2JiYmjfvr0l6L7dyJEjOXPmDLt27QJg8eLFDBo0CHt76aYjhBDFCvgr1G4F+Vmw5YO7H2s20SZjI+OVrxhd8zDPNLUnNMCDkAa1CPAyUremHa6OBl7p5s+68e0J9qlJdr6JmetO0Gf+Ng6cv/ZgnkmIh5zUeJen2M3qZ4POnI5VB1bzc5MabyEAbK11HJsRWmn3Li+3B4OTJk1i48aNzJ49m4YNG2Jra8vAgQPvOfDY7UGoRqPBbDbf8fiIiAiuXr2Kra2tZZvZbObQoUNMnz690Pbi3Gu/Vqst0iQ/P7/oSLi3P39WVhahoaGEhobyv//9D1dXV86fP09oaKglD+51bzc3N/r06UNkZCS+vr6sW7dO5nUWQoi70Wig23sQ2R0OLIU2Y8GtSdHjYjfBhqlw6citJ4NHM/DtqPY99w4BG3UapIZujkSNeYLv9l/gg3XHOZGUwYCFOxjWxptXQxtjtC3+BaoQ4t4k8C5Pf6iBt9m3E6d3qFOJ+UmNtxCAGliWV3PvqmT79u2MHDmS/v37A2oN+NmzZ8v1HleuXOGHH34gKiqKgIAAy3aTycSTTz7Jhg0b6N69Oz4+PkRHR9O5c+ci12jRogUXLlzg1KlTxdZ6u7q6kpSUhKIoaG4M+BMTE3PPtJ04cYIrV64wa9Ys6tatC8C+ffuK3HvJkiXk5+ffsdb7+eefZ+jQodSpU4cGDRrQrl27e95bCCEeafVCoEkfOL4GNr4Dw779c1/ycdjwFpy5MdinjTP494SEA5ByApIOq8vOf4FGB15BUPcJcGuC1rUxg5r706VJR97/6TgrDlzkv7vOs+VkCoufC6ahVCoJUSYP31/BleXqH3DtLGitSHBuSXb+XvQ6LT61ymcKISFE1eTn58eKFSvo06cPGo2GqVOn3rXmuiy++uoratWqxaBBgyxB8U09e/YkIiKC7t27M23aNMaOHYubmxs9evQgIyOD7du389JLL9GxY0c6dOjAgAEDmDNnDg0bNuTEiRNoNBq6d+9Op06dSElJ4cMPP2TgwIGsX7+edevW4eTkdNe0eXt7o9frmT9/PmPHjuXIkSO8++67hY4JDw9n/vz5DBkyhClTpmA0Gtm1axfBwcGWkdFDQ0NxcnLivffeY8aMGeWaf0II8dDqOh1OroPTP8MfW8CtKWz+AA4sUbs/aq3Vwdg6vKqOiA6QcQnO/qZ2kYz7Fa7FwcX96nKLWk61mePamEmP+bA01oYV15oyYOEOvgxrRWufmg/+WYWo5qSPd3m52cy8TjAnr6nNNeu72mMlA1II8VCbM2cONWrUoG3btvTp04fQ0FAef/zxcr3HokWL6N+/f5GgG2DAgAGsXr2ay5cvExYWxty5c/nss88ICAigd+/enD592nLs999/T+vWrRk6dChNmzbltddew2QyAdCkSRM+++wzFixYQGBgIHv27Ck0b/mduLq6snjxYpYvX07Tpk2ZNWsWs2fPLnRMrVq12LRpE5mZmXTs2JGWLVvyxRdfFKr91mq1jBw5EpPJxIgRI8qaVUII8Wip1QBajVa/rxoH8x6D/ZFq0N2kjzp1WfeZfwbdoA7E1nwg/GUejI+BCUeg30K1uXr9TuBwY9T19IsQG43X8Qgm5y1gk+3rNMg5yrAvd/PjoYTye4brV+FKbPldT4gqSqM8BJP1paenYzQaSUtLu2ftTIX5Zrja1KfzmyxUBvDP9SfoE+jF/KGPVU56hKhkOTk5lhG3bWxsKjs5ohoYPXo0KSkpJZrTvKq42+95lSibHjKSp0IUI+sKzAuC3HR13etxCH0f6rUt+zWzr0HKSbVZevIJtTtlyglyNTa8kDueX82BvNmzCc+39y32pXCxrl+9cc3j6jVTbiyZl9T9HSdD5yllT7MQlaA05ZI0NS8PZpPaVAegfmdO71AHVmskfWCEEOKe0tLSOHz4MF9//XW1CrqFEKJKsK+l1l7v+VKdWqzZANDeZ4tL2xrg/YS6AORlwbcjMJz5hUWGjxmf+yLv/wQXU7OZ2rspOu0dgm+zCfZ+qU57ln7x7vfcOgvsXdSm8UI8hCTwLg8JByEnDWyM4PUYp5J3AjKwmhBClETfvn3Zs2cPY8eOLTRHuhBCiBIK6K8uFUVvD0OWwcq/Y3V0Bf/Sz8eYn8XiHV1ISM3m0yGPYau/bQaRhIOwZgIkxvy5zakOuDUG18bqKOyujcHVH3b8Sw28f3oV7GpBs79W3LMIUUkk8C4PN/t3+3bArNFxJlkd0byRu9R4CyHEvcjUYUIIUQ1Y6WHAl2DrjGbfIj6wjqCWNov5x/ow9ItdvN5dnW7MSZON677Z6A98iUYxg8EIXaZCi8GWacuK6DQZslJgXwSsGKPWuDcoOkNHhTDlAxrQSVgkKpb8hpWHG9OIUb8zF65lk5NvRm+lpV4t+7ufJ4QQQgghRHWh1UGvOWBbE36bzSu6KFx0WbwTP4ShX+ykp3Y371gvxaBJBeBHpR2fmUdh3uHGE0nx9GzuSct6NYo2TddooOdHcP0yHPtBHTspbA3ULt/BSgvJz1GbwG+fCwU54OgFznXBWAeMNz6dvcG5Hrj4qWkU4j5I4H2/cjMhfo/6vUFnTiWp/bsbuDrcub+LEEIIIYQQ1ZFGo9Zg29aADW8Sxhoa1ryOLjeVJ0zqlGRxZnemFoxim7k55AIZGZxIymDxjrO4OhroHuBBj+YeBPvU/HMGIK0O/vqFOrBb3K/wv4EwagO4NCz/Zzi5Hta/rk4FfFP6BXUpTvNB0P9zNY1ClJEE3vfr3HYw56tvw2rW59ShM4A0MxdCCCGEEA+xtuFq8L06nHbXo9VtOj08+TJeIeP5tMCKjJwCMnIKSEjLZuOxS2w4mkRKRi5f7TrHV7vOUcteT7cADzr5u6IBsvIKyKk/iy4po3DLPMG1//RiXr3PSLWqhZuTAU8nGzyMNngYbfE02uDiYChdRdfVOFg/GU6tV9cdPaHbe1CvHaRdgLTzNz4vQGq8+plyHA5/C9a20OdTqfkWZSaB9/260b871fNJPvnhCCsOqCM2NpKB1YQQQgghxMPssWHq4MI/vAhej0HP2eDihwEwALUcDAA0r2MkNMCDvP7N2RF7mXWHk/j5WBJXsvJYtuc8y/acL3TZWoznO/00fPOSGHxyPIPyppJO0UotnVaDm6OBxh6ODGtTj6cau6EtLhDPz4Ztc2HbJ2DKBa0VPPEidHwNDDf+ZnfyhLqti557dBV89xwcWAK2zvD0jPvJsdLJzQCNVh3cTlR7Enjfh5x8E3lHN+AETPndhXXmcwD4utjTq7ln5SZOCCGEEEKIitakN/j3LNEUZnorLZ383ejk78Z7pmbs/uMqPx1J5MC5a9jqdTgYrG4sdfiBBTx/6u80zo8n2mUOsXaBpOZbcSVPx+UcHSm5WrLMerIzDBRk6Fh+2syvjtZ08qtFiK8RWyuNOp1ZXhbs/Bekqn+n49tR7U/u6l+y5wvop86RvvoltU+4jTO0n1jm7Cqxs9sgapj6vf/n4N+j4u8pKpQE3qWkKApnkjNZtiee3/b/zkZiMSsadtOMHs08+Fsbb9o1cCn+bZsQQgghhBAPmzLMG26t0/KknwtP+rnc+aCk1RDZE9fME7hmnii8r7goJhc4cmO5nVNtCH0fmvYDjQZFUUi9ns/lzFwAbKx1NxYtNtY6rHW3PNPjI9Spgze8BdHT1dHZWz9fugcujSPfw8qxYMpT15cNgSdfhs5vVZ/R1/Oz1RcfBul+e1M1+ck9eHkFZs5dySI2JYvYlExiUzL548b3jJwCAAbqDoI1pDg1Yf0Lf8HNyaaSUy2EqAo6depEUFAQc+fOBcDHx4cJEyYwYcKEO56j0WhYuXIl/fr1u697l9d1hBBCiErn0Qxe2ARHV0J+lhrM5V+/8XnLd1M+Jo2Wy1kFJKbnk5VnxoQWM1qcHWzIcQ1kg3EgF/brSN6yg5SMXFIycskzme94ayutBhtrHfYGHU81duOF9s9RPzsVfpsNayepNd/NB5bv8yqKWju/4S11vUkf9YXB7s/VZvLxe2FgBDh6lO99y9uZaHVauIJc6DELgoZJ33gk8C5i7aFEZm84yfmr1zGZlWKP0Wk1dPZ3Y5I5Ac6Be1APkKBbiGqvT58+5Ofns379+iL7fvvtNzp06MDvv/9OixYtSnXdvXv3Ym9fvv2zpk2bxqpVq4iJiSm0PTExkRo1apTrve4kOzub2rVro9VquXjxIgaD4YHcVwghxCPEpSF0fPWeh+kAd8BNUdh25jJLtp9l08lklGvANYDLxZ5ntLVGq4HsfBM5+X8G4gVmhczcAjJzC1i2J56ovfGENunBu02ScT2+FFb+HfQO4N+9PJ5SrR3++U3YvVBdbzMWQj9QR1L3fgJ+CIdz2+Dz9jBwEfi2L5/7liezCbbMgl8/Am7EUT+MgxM/qQPTObhWavIqmwTet9FpIe5yFgAOBivqu9rTwNWBBq721Hd1oIGrA/Vq2WGj08DHu9STGjxViSkWQpSX0aNHM2DAAC5cuECdOnUK7YuMjKRVq1alDroBXF0fXEHj4fHg3oJ///33BAQEoCgKq1atYvDgwQ/s3rdTFAWTyYSVlRRrQgjxKNNoNLT3c6W9nytnL2cRtTee1Ot5uDkacHU04Opog5uTwbJusPpzijBFUcgtMJNzIwjPzjdx8Vo2kdvjiD6RzPpjl/iZbiytEU/77M0oy8PQDP8efJ4Es1mdhzw94cZyUf005an9yn07gPUdKurys9Ua4uOr1fVu70FI+J+1xAH9wb0ZfDsCko/B0r/AU29Bu5fL1My/QmRcgu9Hw9nf1PWWI9VZnzZ/ACfXQvxu+Ms8aNyrUpNZmarIT6rqCPatxdfPt2H3G104PK0bq8Of5JPBQYQ/5UfP5p74ezhiY62D5KOQlQLW9lAnuLKTLYQoB71798bV1ZXFixcX2p6Zmcny5csZPXo0V65cYejQodSuXRs7OzuaN2/OsmXL7npdHx8fS7NzgNOnT9OhQwdsbGxo2rQpGzduLHLO66+/TqNGjbCzs6N+/fpMnTqV/Px8ABYvXsz06dP5/fff0Wg0aDQaS5o1Gg2rVq2yXOfw4cM89dRT2NraUqtWLcaMGUNmZqZl/8iRI+nXrx+zZ8/G09OTWrVqMW7cOMu97iYiIoLhw4czfPhwIiIiiuw/evQovXv3xsnJCUdHR9q3b09sbKxl/6JFiwgICMBgMODp6Ul4eDgAZ8+eRaPRFKrNT01NRaPRsGXLFgC2bNmCRqNh3bp1tGzZEoPBwLZt24iNjaVv3764u7vj4OBA69at+eWXXwqlKzc3l9dff526detiMBho2LAhERERKIpCw4YNmT17dqHjY2Ji0Gg0nDlz5p55IoQQourwcbFnco/GzBrQgond/Hk2xIfuzTx43LsGdWrYFQq6QS1Dbax1ONvp8TDa4Otiz5N+LkSMbM3PEzow4PE66LQ6nrv2HBtNj6MpyKHgq4GYP2kO77nBbD/4T0eIGgo/TYJtc9Sm418/Ax/6wrK/wYGlapB60/WrsLSfGnTr9DAgAtq+VLRptosfPB8NgX8DxQzRM9S+31lXKj4j7yXuV/j8STXotraHv36p1nC3nwhjNoNbgPpSIupvag14Tnplp7hSSNXAbWra62nb8C6DPNx0YxoxfNqBlb5iEyXEw0BR1L5YlcHarkR9i6ysrBgxYgSLFy/mzTffRHPjnOXLl2MymRg6dCiZmZm0bNmS119/HScnJ9auXcuzzz5LgwYNCA6+90s4s9nMX//6V9zd3dm9ezdpaWnF9v12dHRk8eLFeHl5cfjwYV544QUcHR157bXXGDx4MEeOHGH9+vWWoNJoNBa5RlZWFqGhoYSEhLB3716Sk5N5/vnnCQ8PL/RyYfPmzXh6erJ582bOnDnD4MGDCQoK4oUXXrjjc8TGxrJz505WrFiBoii8/PLLnDt3jnr16gFw8eJFOnToQKdOndi0aRNOTk5s376dggJ1jIyFCxcyceJEZs2aRY8ePUhLS2P79u33zL/bTZ48mdmzZ1O/fn1q1KhBfHw8PXv25P3338dgMLB06VL69OnDyZMn8fb2BmDEiBHs3LmTefPmERgYSFxcHJcvX0aj0TBq1CgiIyOZNGmS5R6RkZF06NCBhg0bljp9QgghHg7+Ho58PCiQV7o1YtG2OF7f8zIOppmEcEyd/xtQ0JBn44KVc210xtrg5KXWeJ/eCBkJas3vybXqBWu3BL9ucPg7uHIaDEYY8r+7NyHX20G/z6BeiNrP/PTPMKexWqPepDf497p7c25FUWvMT62HUz9D0hFo0Bna/h94tyl9ppjNap/3LTPVlwFuTeGZJeDa6M9jPJqrwfem92DHfDj4XzVQ7/e5Gkc9QjSKohTfkbkaSU9Px2g0kpaWhpOT04O56Vf9IXYThM6EkBcfzD2FqEZycnKIi4vD19cXGxsbdTqPD7wqJzFvJJR4DswTJ07QpEkTNm/eTKdOnQDo0KED9erV46uvvir2nN69e9O4cWNLTendBlfbsGEDvXr14ty5c3h5qfmxfv16evTocddB0WbPnk1UVBT79u0D7tzH+9bB1b744gtef/114uPjLX3Mf/rpJ/r06UNCQgLu7u6MHDmSLVu2EBsbi06nvvkfNGgQWq2WqKioO+bTm2++ybFjx1i5ciUA/fr1IygoiGnTpgHwxhtvEBUVxcmTJ7G2ti5yfu3atXnuued47733iuw7e/Ysvr6+HDx4kKCgIECt8a5Ro4bl57JlyxY6d+7MqlWr6Nu37x3TCdCsWTPGjh1LeHg4p06dwt/fn40bN9K1a9cixyYkJODt7c2OHTsIDg4mPz8fLy8vZs+eTVhYWJHji/ye36JSyqaHnOSpEKKqSLuez9c7z3Bq7wbOpZlJUmqSjDMFWKHVQICXkSfq16RZbSO5eSb0l4/inrSZepd/xev68ULXuqxz5Z813+Oi3geNBjRo1E+Nhlr2egK8nGjq5USAlxGj7Y0yNfEQrPoHXLplCHeNFuo+oQbhjXtDjXpqM/a439Qg/dTPkBZf/APVaa3WtDfurfYrv5dr5+DHCWo8BPDYcOjxkfpy4E7ObodVYyH1PKCB1qOh3QRwrnvv+1VRpSmXpMa7LPJz4NwO9XuDzpWbFiFEuWrcuDFt27Zl0aJFdOrUiTNnzvDbb78xY8YMAEwmEx988AHffvstFy9eJC8vj9zcXOzs7lLQ3OL48ePUrVvXEnQDhISEFDnum2++Yd68ecTGxpKZmUlBQUGpA43jx48TGBhYaGC3du3aYTabOXnyJO7u7gAEBARYgm4AT09PDh8+fMfrmkwmlixZwqeffmrZNnz4cCZNmsTbb7+NVqslJiaG9u3bFxt0Jycnk5CQQJcuXUr1PMVp1apVofXMzEymTZvG2rVrSUxMpKCggOzsbM6fV2sjYmJi0Ol0dOzYsdjreXl50atXLxYtWkRwcDBr1qwhNzeXZ5555r7TKoQQ4uFhtLPmH12aQJcmJKZls/uPq+z64wq7/rjC2SvXOXwxjcMX0247qyPQETeu8ZTuIF20B9FhYkrO81zKcgSKbza+8uBFy/e6NW0J8DQS4OVEQKfvqWuKp+b5DTicXY8h+RCc36EuP7+B4tIIUuPRFGRbzjdpDVyo0YbjjiGct6pHSPrPNL38E7oLe+HbEZidfdC2DYegv/1ZaVGQB0mH1X7aF/ZA/B61DzuAlS30nqMefy8+7WDsdvh5ilrzvfdL2L8Ymj8D7caDW5MS5391JIF3cTKS7j5Mf/wuKMgBR09wbfzg0iVEdWZtp9Y8V9a9S2H06NG89NJLLFiwgMjISBo0aGAJ1D766CM+/fRT5s6dS/PmzbG3t2fChAnk5eWVW3J37tzJsGHDmD59OqGhoRiNRqKiovj444/L7R63uj041mg0mM13nmLl559/5uLFi0UGUzOZTERHR/P0009ja2t7x/Pvtg9Ae2OgmFsbZN2pz/nto8VPmjSJjRs3Mnv2bBo2bIitrS0DBw60/HzudW+A559/nmeffZZPPvmEyMhIBg8eXOIXK0IIIR49nkZb+j1Wm36P1QYgKS2H3XFqEP5HShYOBiscbKwsn44GKxwM7UizscZgpeUt1DHAb5Z7ZkVBUdSW4Qmp2RxNSOdIQhoXrmUTf1Vd1h9NuiUFQUAQtUmhm24fobp9tNacQHf5FAAXlVpsNgURbX6cneam5Fy/dRaSwbgSyrNWG3hW9ws1Us/CT5NIXzed3+3b4VFwgXq5p9Arhf/OMaHlvH1z4trMoFn9J3ArQT79kZLJuiPJrDs/BMe8eoy3/oEnzEfg92Xq0qgHPDlBHcX9ISSB9+0SDsIXT4FfKLR6Dhp2Ldrc4mb/7vqdZE46IUpKoylxc+/KNmjQIMaPH8/XX3/N0qVL+cc//mHp7719+3b69u3L8OHDAbXP9qlTp2jatGmJrt2kSRPi4+NJTEzE09MTgF27dhU6ZseOHdSrV48333zTsu3cuXOFjtHr9ZhMpnvea/HixWRlZVkC1O3bt6PVavH39y9ReosTERHBkCFDCqUP4P333yciIoKnn36aFi1asGTJEvLz84sE9o6Ojvj4+BAdHU3nzkVbDd0cBT4xMZHHHnsMoEiT+jvZvn07I0eOpH///oBaA3727FnL/ubNm2M2m9m6dWuxTc0Bevbsib29PQsXLmT9+vX8+uuvJbq3EEIIAeBhtKFvUG36BtUu1+umXc/naGIaRy+mczQhjWOJ6aRezyfPZCY330xigSuRph5EmnpQk3Raa09wTvHgrFU9ajnYUMPemmB7AzXtrKlpb8DBoCMlM4+EVFfWpNZhSWp/ehZs5nndT9TTJtM+88/pVa8pDuw3+3HA3IgDih+/m+uTnWMDP2XBT9H4uTnQtkEtQhq48ET9mjjbqWNgnUnO4KfDSfx0OJETSRm3PE0AO3MDaKGJ5UXrNXTT7kV7ah2cWqc2l39yAngGqX3kTfk3Pm/5XpAD2dcg67I6cNv1Kze+3/jMz1YHpPNsofYz92gBNeuXrBl9BZHA+3Znt6mDA9z8wRvrQssweOzZP2vB/7gZeEszcyEeRg4ODgwePJgpU6aQnp7OyJEjLfv8/Pz47rvv2LFjBzVq1GDOnDlcunSpxIF3165dadSoEWFhYXz00Uekp6cXCWD9/Pw4f/48UVFRtG7dmrVr11r6Ut/k4+NDXFwcMTEx1KlTB0dHxyLzaA8bNox33nmHsLAwpk2bRkpKCi+99BLPPvuspZl5aaWkpLBmzRpWr15Ns2bNCu0bMWIE/fv35+rVq4SHhzN//nyGDBnClClTMBqN7Nq1i+DgYPz9/Zk2bRpjx47Fzc2NHj16kJGRwfbt23nppZewtbXliSeeYNasWfj6+pKcnMxbb71VovT5+fmxYsUK+vTpg0ajYerUqYVq7318fAgLC2PUqFGWwdXOnTtHcnIygwYNAkCn0zFy5EimTJmCn59fsV0BhBBCiAfNaGdN2wYutG1w54GgC0xm8kxm8grUxdHGGlt9yYJNRVFIz+7FxavT+P3YGvSJ+0h1aEiycyDptt4oGg3uCnRXFEJRa+N3/nGFownpnE7O5HRyJkt2nkOjgQAvJ3LzzZxO/nMmFSuthrYNXejZzIOnm7pz6GIaCzfXZOzZBvhqEvm71Y8MsNqGdfwudcT2+5V2HmKj/1y3tgf3gBuBeHO1gvUB9i+XwPt2bV+CRt3V/gYx/1MHINj0njoZvH9PaD5QHcwA1BpvIcRDafTo0URERNCzZ89C/bHfeust/vjjD0JDQ7Gzs2PMmDH069ePtLTb+3EVT6vVsnLlSkaPHk1wcDA+Pj7MmzeP7t27W475y1/+wssvv0x4eDi5ubn06tWLqVOnWgYuAxgwYAArVqygc+fOpKamEhkZWegFAYCdnR0///wz48ePp3Xr1tjZ2TFgwADmzJlT5nxZunQp9vb2xfbP7tKlC7a2tvz3v//l//7v/9i0aROvvvoqHTt2RKfTERQURLt26gimYWFh5OTk8MknnzBp0iRcXFwYOHCg5VqLFi1i9OjRtGzZEn9/fz788EO6det2z/TNmTOHUaNG0bZtW1xcXHj99ddJTy88bcnChQt54403ePHFF7ly5Qre3t688cYbhY4ZPXo0H3zwAc8991xZskkIIYSoFFY6LVY6LXZlmHRJo9FgtLPGaFcD6owARpTovGtZeeyOu8KOWHU5k5zJkYtq2WutU+dV73Ej2Ha+JWGd/d3o7O/GvrNXWbgllsknPJmTP5BRVut41noLtmRj1lqjaK1Bp0ej06O11qOxMqDR6cG2BmbbWuQZapBt5UymlTPpWiOpGkeyTVZ45sbhmnUKp7TjGK6cQJOfpfZRv7BHTcDAyAcaeMuo5neTnwPHfoB9i9R+3bdyC4AXd5TfvYR4yNxttGchqrrffvuNLl26EB8ff9fWATKq+YMleSqEEFVfcnoOO/+4gkajoWMj1z9HYr+HE0npfL4lljWHEjGZzUDxXXoNVlqcbK3JzjORmVtQomtrMdNAm0hrw0UCrc/TmLOYe33MY4GPl/SxiiWjmpcXaxsIHKwul47Cvkg49A3kpkPTv1R26oQQQpSz3NxcUlJSmDZtGs8880yZm+QLIYQQjyo3J5sy9W9v7OHE3CGPMfFpf6L2nudiajZXs/K4mpXHtaw8rmTlkVtgJrfATEpGbqFzHQxWGG2tcbK1xtlWbV6fel0990pWHhk5BZw21+Z0dm2+zg4GINKmTrk8b0lJ4F1S7gHQazY8PV2dbL72/b0dEUIIUfUsW7aM0aNHExQUxNKlSys7OUIIIcQjx7uWHa91LzpzlKIoZOebuJKZR1p2PvY3g20bK6x02rteM6/ATOp1NQi/GYw38zJW1CMUSwLv0tLbg3ebyk6FEEKICjBy5MgifeWFEEIIUfk0Gg12eivsalpR2p7Zeistbk42uDlVXvfHu78aEEIIIYQQQgghxH2RwFsIIYQQQgghhKhAEngLISrUQzBxghB3dOsc4Y+iBQsW4OPjg42NDW3atGHPnj13PX758uU0btwYGxsbmjdvzk8//fSAUiqEEEJULunjLYSoENbW1mg0GlJSUnB1dUWjKX5KCCGqI0VRyMvLIyUlBa1Wi15fhglTq7lvvvmGiRMn8vnnn9OmTRvmzp1LaGgoJ0+exM3NrcjxO3bsYOjQocycOZPevXvz9ddf069fPw4cOECzZs0q4QmEEEKIB6dM83gvWLCAjz76iKSkJAIDA5k/fz7BwcHFHtupUye2bt1aZHvPnj1Zu3YtoP4B88477/DFF1+QmppKu3btWLhwIX5+fiVKj8zrKUTVlJmZyYULF6TWWzy07Ozs8PT0LDbwftjLpjZt2tC6dWv+9a9/AWrtf926dXnppZeYPHlykeMHDx5MVlYWP/74o2XbE088QVBQEJ9//nmJ7vmw56kQQojqpULn8S7tG+4VK1aQl5dnWb9y5QqBgYE888wzlm0ffvgh8+bNY8mSJfj6+jJ16lRCQ0M5duwYNjaVN/KcEOL+ODg44OfnR35+fmUnRYhyp9PpsLKyeiRbc+Tl5bF//36mTJli2abVaunatSs7d+4s9pydO3cyceLEQttCQ0NZtWrVHe+Tm5tLbu6fc7Wmp6ffX8KFEEKISlLqwHvOnDm88MILPPfccwB8/vnnrF27lkWLFhX7hrtmzZqF1qOiorCzs7ME3oqiMHfuXN566y369u0LwNKlS3F3d2fVqlUMGTKk1A8lhKg6dDodOp2uspMhhChHly9fxmQy4e7uXmi7u7s7J06cKPacpKSkYo9PSkq6431mzpzJ9OnT7z/BQgghRCUr1eBqN99wd+3a9c8L3OMN9+0iIiIYMmQI9vb2AMTFxZGUlFTomkajkTZt2tzxmrm5uaSnpxdahBBCCPFwmTJlCmlpaZYlPj6+spMkhBBClEmpAu+7veG+2xvrm/bs2cORI0d4/vnnLdtunleaa86cOROj0WhZ6tYt7RTqQgghhCgrFxcXdDodly5dKrT90qVLeHh4FHuOh4dHqY4HMBgMODk5FVqEEEKI6uiBTicWERFB8+bN7zgQW0nJG3AhhBCi8uj1elq2bEl0dLRlm9lsJjo6mpCQkGLPCQkJKXQ8wMaNG+94vBBCCPEwKVUf77K84b4pKyuLqKgoZsyYUWj7zfMuXbqEp6dnoWsGBQUVey2DwYDBYLCs3xwxWZqcCyGEqCpulkkP66j+EydOJCwsjFatWhEcHMzcuXPJysqyjAEzYsQIateuzcyZMwEYP348HTt25OOPP6ZXr15ERUWxb98+/vOf/5T4nlLeCyGEqEpKVdYrpRQcHKyEh4db1k0mk1K7dm1l5syZdz0vMjJSMRgMyuXLlwttN5vNioeHhzJ79mzLtrS0NMVgMCjLli0rUZri4+MVQBZZZJFFFlmq3BIfH1+KUrZ6mT9/vuLt7a3o9XolODhY2bVrl2Vfx44dlbCwsELHf/vtt0qjRo0UvV6vBAQEKGvXri3V/aS8l0UWWWSRpSouJSnrSz2P9zfffENYWBj//ve/LW+4v/32W06cOIG7u3uRN9w3tW/fntq1axMVFVXkmv/85z+ZNWtWoenEDh06VOLpxMxmMwkJCTg6OpbLtC7p6enUrVuX+Ph46U9WCpJvZSd5VzaSb2Uj+VZ2pck7RVHIyMjAy8sLrfaB9ux6aJVneS//DspG8q3sJO/KRvKt7CTvyqaiyvpSTyc2ePBgUlJSePvtt0lKSiIoKIj169dbBkc7f/58kZuePHmSbdu2sWHDhmKv+dprr5GVlcWYMWNITU3lySefZP369SWew1ur1VKnTp3SPso9yUAuZSP5VnaSd2Uj+VY2km9lV9K8MxqNDyA1j46KKO/l30HZSL6VneRd2Ui+lZ3kXdmUd1lf6sAbIDw8nPDw8GL3bdmypcg2f3//u7Z712g0zJgxo0j/byGEEEIIIYQQorqTtm9CCCGEEEIIIUQFksC7GAaDgXfeeafQyOni3iTfyk7yrmwk38pG8q3sJO8eHvKzLBvJt7KTvCsbybeyk7wrm4rKt1IPriaEEEIIIYQQQoiSkxpvIYQQQgghhBCiAkngLYQQQgghhBBCVCAJvIUQQgghhBBCiAokgbcQQgghhBBCCFGBJPC+zYIFC/Dx8cHGxoY2bdqwZ8+eyk5SlfPrr7/Sp08fvLy80Gg0rFq1qtB+RVF4++238fT0xNbWlq5du3L69OnKSWwVMnPmTFq3bo2joyNubm7069ePkydPFjomJyeHcePGUatWLRwcHBgwYACXLl2qpBRXDQsXLqRFixY4OTnh5ORESEgI69ats+yXPCuZWbNmodFomDBhgmWb5F3xpk2bhkajKbQ0btzYsl/yrfqTsv7epKwvGynry07K+/Ih5X3JVEZZL4H3Lb755hsmTpzIO++8w4EDBwgMDCQ0NJTk5OTKTlqVkpWVRWBgIAsWLCh2/4cffsi8efP4/PPP2b17N/b29oSGhpKTk/OAU1q1bN26lXHjxrFr1y42btxIfn4+3bp1Iysry3LMyy+/zJo1a1i+fDlbt24lISGBv/71r5WY6spXp04dZs2axf79+9m3bx9PPfUUffv25ejRo4DkWUns3buXf//737Ro0aLQdsm7OwsICCAxMdGybNu2zbJP8q16k7K+ZKSsLxsp68tOyvv7J+V96Tzwsl4RFsHBwcq4ceMs6yaTSfHy8lJmzpxZiamq2gBl5cqVlnWz2ax4eHgoH330kWVbamqqYjAYlGXLllVCCquu5ORkBVC2bt2qKIqaT9bW1sry5cstxxw/flwBlJ07d1ZWMqukGjVqKF9++aXkWQlkZGQofn5+ysaNG5WOHTsq48ePVxRFft/u5p133lECAwOL3Sf5Vv1JWV96UtaXnZT190fK+5KT8r50KqOslxrvG/Ly8ti/fz9du3a1bNNqtXTt2pWdO3dWYsqql7i4OJKSkgrlo9FopE2bNpKPt0lLSwOgZs2aAOzfv5/8/PxCede4cWO8vb0l724wmUxERUWRlZVFSEiI5FkJjBs3jl69ehXKI5Dft3s5ffo0Xl5e1K9fn2HDhnH+/HlA8q26k7K+fEhZX3JS1peNlPelJ+V96T3ost7qvlP8kLh8+TImkwl3d/dC293d3Tlx4kQlpar6SUpKAig2H2/uE2A2m5kwYQLt2rWjWbNmgJp3er0eZ2fnQsdK3sHhw4cJCQkhJycHBwcHVq5cSdOmTYmJiZE8u4uoqCgOHDjA3r17i+yT37c7a9OmDYsXL8bf35/ExESmT59O+/btOXLkiORbNSdlffmQsr5kpKwvPSnvy0bK+9KrjLJeAm8hKsG4ceM4cuRIob4k4s78/f2JiYkhLS2N7777jrCwMLZu3VrZyarS4uPjGT9+PBs3bsTGxqayk1Ot9OjRw/K9RYsWtGnThnr16vHtt99ia2tbiSkTQlQnUtaXnpT3pSflfdlURlkvTc1vcHFxQafTFRmt7tKlS3h4eFRSqqqfm3kl+Xhn4eHh/Pjjj2zevJk6depYtnt4eJCXl0dqamqh4yXvQK/X07BhQ1q2bMnMmTMJDAzk008/lTy7i/3795OcnMzjjz+OlZUVVlZWbN26lXnz5mFlZYW7u7vkXQk5OzvTqFEjzpw5I79z1ZyU9eVDyvp7k7K+bKS8Lz0p78vHgyjrJfC+Qa/X07JlS6Kjoy3bzGYz0dHRhISEVGLKqhdfX188PDwK5WN6ejq7d+9+5PNRURTCw8NZuXIlmzZtwtfXt9D+li1bYm1tXSjvTp48yfnz5x/5vLud2WwmNzdX8uwuunTpwuHDh4mJibEsrVq1YtiwYZbvknclk5mZSWxsLJ6envI7V81JWV8+pKy/Mynry5eU9/cm5X35eCBlfZmHZXsIRUVFKQaDQVm8eLFy7NgxZcyYMYqzs7OSlJRU2UmrUjIyMpSDBw8qBw8eVABlzpw5ysGDB5Vz584piqIos2bNUpydnZUffvhBOXTokNK3b1/F19dXyc7OruSUV65//OMfitFoVLZs2aIkJiZaluvXr1uOGTt2rOLt7a1s2rRJ2bdvnxISEqKEhIRUYqor3+TJk5WtW7cqcXFxyqFDh5TJkycrGo1G2bBhg6Iokmelcesop4oieXcnr7zyirJlyxYlLi5O2b59u9K1a1fFxcVFSU5OVhRF8q26k7K+ZKSsLxsp68tOyvvyI+X9vVVGWS+B923mz5+veHt7K3q9XgkODlZ27dpV2UmqcjZv3qwARZawsDBFUdRpRqZOnaq4u7srBoNB6dKli3Ly5MnKTXQVUFyeAUpkZKTlmOzsbOXFF19UatSoodjZ2Sn9+/dXEhMTKy/RVcCoUaOUevXqKXq9XnF1dVW6dOliKYQVRfKsNG4viCXvijd48GDF09NT0ev1Su3atZXBgwcrZ86cseyXfKv+pKy/Nynry0bK+rKT8r78SHl/b5VR1msURVHKXl8uhBBCCCGEEEKIu5E+3kIIIYQQQgghRAWSwFsIIYQQQgghhKhAEngLIYQQQgghhBAVSAJvIYQQQgghhBCiAkngLYQQQgghhBBCVCAJvIUQQgghhBBCiAokgbcQQgghhBBCCFGBJPAWQgghhBBCCCEqkATeQgghhBBCCCFEBZLAWwghhBBCCCGEqEASeAshhBBCCCGEEBVIAm8hhBBCCCGEEKIC/T944WaJRhSHnwAAAABJRU5ErkJggg==\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Generate predictions\n",
        "y_pred = (model.predict(X_test) > 0.5).astype(int)\n",
        "\n",
        "# Classification report\n",
        "print(\"\\nClassification Report:\")\n",
        "print(classification_report(y_test, y_pred))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ephYKrIxRu7e",
        "outputId": "13444326-9554-4b05-8732-ab891690e334"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1m137/137\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 9ms/step\n",
            "\n",
            "Classification Report:\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "         0.0       0.99      0.98      0.99      1189\n",
            "         1.0       0.99      1.00      1.00      3176\n",
            "\n",
            "    accuracy                           0.99      4365\n",
            "   macro avg       0.99      0.99      0.99      4365\n",
            "weighted avg       0.99      0.99      0.99      4365\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import classification_report, confusion_matrix\n",
        "import seaborn as sns\n",
        "\n",
        "# Confusion matrix\n",
        "conf_matrix = confusion_matrix(y_test, y_pred)\n",
        "sns.heatmap(conf_matrix, annot=True, fmt='d', cmap='Blues', xticklabels=['Normal', 'Abnormal'], yticklabels=['Normal', 'Abnormal'])\n",
        "plt.title('Confusion Matrix')\n",
        "plt.ylabel('True Label')\n",
        "plt.xlabel('Predicted Label')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "5WklYT4vRjLe",
        "outputId": "d7bc4117-b181-4fdb-a416-ab9e01ea1e07"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Save the model\n",
        "model.save('/content/drive/MyDrive/ecg_model.h5')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jaVA1fpaC7Ln",
        "outputId": "8d4c9800-765b-4745-baf6-38477d473ba5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:absl:You are saving your model as an HDF5 file via `model.save()` or `keras.saving.save_model(model)`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')` or `keras.saving.save_model(model, 'my_model.keras')`. \n"
          ]
        }
      ]
    }
  ]
}